Every individual responds differently to medications, and their reactions can vary greatly. While a specific drug may be effective for one person with no adverse effects, another individual may experience a negative reaction to the same medication. Pharmacogenomic (PGx) testing enables the identification of patients who are prone to adverse drug reactions, individuals who are more likely to benefit from specific medications and those who may require adjusted dosages. As adverse drug reactions rank as the fourth leading cause of death in the United States, pharmacogenomics plays a crucial role in patient care by utilizing genetic information to optimize medication strategies. By applying this knowledge, healthcare providers can tailor medication plans to individual patients, maximize treatment outcomes and minimize potential risks.

The PREPARE Study

A landmark study conducted in Europe coined the PREPARE study (Pre-emptive Pharmacogenomic Testing for Preventing Adverse Drug Reactions) has furthered the discussion regarding the large-scale deployment and value of PGx testing, precision medicine, and preventative healthcare around the world. The PREPARE study was a randomized, international, multi-facility study with 7,000 patients across the healthcare industry. The results were impressive, showcasing significant harm reduction to patients who benefited from PGx testing through a 30% decrease in clinically significant adverse drug reactions. Among other data, the PREPARE study’s results were consistent with existing and expected data from other published PGx studies, showing a repeatable finding from the implementation of PGx testing in real-world practice. 

One major genetic marker included in the PREPARE study, DPYD, has been a focus for PGx testing more recently, as it is associated with a higher risk of adverse drug reactions from common chemotherapy treatments. The study’s findings showed that the prevalence of DPD deficiency, a condition related to this gene, was as expected, emphasizing the need for oncologists to include testing for DPYD before prescribing therapy to their patients.

Applied PGx

In the United States, some hospitals have already started implementing proactive testing measures. For example, they test for DPYD genetic variations in any patient diagnosed with a solid tumor. For organizations who use PGx testing for chemotherapy, it makes sense for those tests to also include other clinically actionable pharmacogenes in a single, wide-spectrum PGx test. By starting with an all-encompassing test, providers can get all genomic biomarkers at once rather than re-testing every time a new prescription is considered for the patient.  

Other hospitals have taken their testing a step further and include additional pharmacogenes, such as UGT1A1, in the same PGx test, covering two genes with one sample. This is beneficial because many “chemo cocktails” used in solid tumor treatment protocols rely on medications affected by both the DPYD and UGT1A1 genes. By testing for both genes in one overarching test, physicians can better prevent negative reactions to drugs and improve patient safety. This pre-emptive multi-gene panel testing has already started developing in several institutions, like UCSF, which recently launched its PGx program to proactively aid patient care.

For oncology, while pre-chemotherapy testing is vital to proper treatment and protection for the patient, there are other aspects of PGx testing that can benefit both the organization and the patient. For example, it’s well known that many patients undergoing cancer treatment will also be prescribed other medications, such as analgesics, antidepressants, and anti-nausea agents. By incorporating PGx testing into the treatment plan, healthcare providers can optimize the selection and dosage of these medications, ensuring better overall patient outcomes and minimizing potential drug interactions or adverse effects.

Implementing Large-Scale Testing

The PREPARE study demonstrated that international healthcare systems can successfully implement and use technologies and programs that support the real-world addition of PGx testing into daily operations and patient care. With therapeutic areas like oncology already using PGx testing and precision medicine in standard practice, this is paving the way for other industries to follow suit. As institutions begin to implement precision medicine, it is important to focus on finding a solution that provides programs and technologies tailored to their end users while still being able to support delivery at scale.

In addition, the PREPARE study proved that PGx testing and precise prescribing are ready and feasible at large scale, operationally and logistically. The current healthcare infrastructure can provide efficient testing logistics, quality tests that cover clinically relevant genetic markers, understandable and impactful data analysis, and proper decision support tools that lead to positive results for patients. However, it is now up to the healthcare industry to embrace precision medicine, broaden the use of PGx testing modalities, and utilize the available tools to support their preventative healthcare programs.

Empowering Our Health Systems

We need to empower physicians with programs and systems that support their workflow, inform which genomic markers to test based on clinically actionable data, adjust the data accordingly to various patient variables, and provide results consistently packaged in a way that can remain permanently within the healthcare provider’s systems for ease of access throughout the care continuum. For institutions, simply adding the ability to order a PGx test is the first step. Institutions need access to technologies that integrate within the prescribing workflow, give insights that are understandable to the healthcare team, and provide actionable information so steps are taken to protect the patient before prescriptions are written, or treatments are changed. As supported by the PREPARE study results, the creation of a comprehensive precision medicine program will lead those institutions to significant harm reduction for their patients.

A Shift to Proactive Care

As the healthcare industry continues to shift from a focus on reactive care to one of proactive healthcare, the results from general PGx testing will continue to play a more important role in diagnosis and care. Results of the PREPARE study reinforce the overwhelming benefit of proactive testing and the proven ability to apply these practices across health systems. Bolstered by these clinically proven results, as well as the general availability of the technology needed to support healthcare teams and patient understanding and utilization, our health systems are now ready to begin the process of proactive treatment optimization using patient genetic signatures.

Houda Hachad, PharmD, M. Res., Vice President of Clinical Operations, Aranscia

Houda is a widely recognized leader in the field of pharmacogenomics and has spearheaded multiple efforts to translate scientific requirements into practical technology-based solutions.  Houda serves on the Clinical Pharmacogenetics Implementation Consortium (CPIC) Scientific Advisory Board and is an active member of the Pharmacogene Variation Consortium (PharmVar). She is also involved with several pharmacogenomics working groups and committees aimed at standardizing pharmacogenomic testing modalities and at facilitating their adoption by the clinical community.

What You Should Know: 

• Prenosis, Inc., a Chicago, IL-based AI company enabling precision medicine in acute care, announced today that it has been awarded two Phase 2 SBIR grants totaling $4.8M in funding by the National Institute of General Medical Sciences (NIGMS), a division of the National Institute of Health (NIH). 
• The grants will study the use of Prenosis’s Immunix™ Artificial Intelligence platform for acute immune states. Prenosis has built a collection of artificial intelligence algorithms, broad clinical data, deep biological data, and biobanked samples of patients suspected of sepsis, in addition to detailed information about their treatment regime. The goal is to better understand how patients’ health states rapidly evolve in acute care environments.
• The grants are titled “Combined Biomarker and EMR Data for Heterogeneous Treatment Effects and Surrogate Endpoints in Sepsis”, and “Use of Time Series Biomarker and Clinical Data to Construct a Time Trajectory Host Response Map”.

Though we seldom see their use in our modern world and, even then, only in fiction, there was a time when it was common for people to actually use things like crystal balls and divining rods to try to uncover unknown yet valuable information. As unbelievable as it may seem, soothsayers peered into crystal balls aiming to help seekers look into the future for guidance, while prospectors would rely earnestly on divining rods as they attempted to locate underground riches of water or oil.

While we may still entertain such images in some of the literature, TV, and movie fantasies we enjoy, in our modern professional world, we tend to entrust industry predictions to those with real, practical knowledge of the business landscape, because they trek, mine, and drill there regularly.

The world of medtech is no different, and for me and my team, connected care is the ground we travel, excavate, and explore on a daily basis. As we venture further into 2023, here’s our perspective on some of the connected care trends we expect to see on the road ahead, from digital therapeutics to remote patient monitoring and clinical trial management.

Precision engagement is an emerging development within digital therapeutics

One of the fast-growing categories within medicine today is digital therapeutics (DTx), which is the delivery of evidence-based treatment through digital solutions that help prevent, manage, or treat a disorder or disease. One recent report valued the global DTx market at $4.2 billion in 2021 and predicted it would expand at a compound annual growth rate of 26.1% between 2022 and 2030, with other estimates projecting even faster growth.

Within DTx, the emergence of precision engagement is a development that holds great promise, especially for chronic conditions where day-to-day choices and behaviors have a significant impact on health outcomes—conditions like diabetes, obesity, and hypertension.

While remote patient monitoring is clearly important for giving care teams visibility into the management of a patient’s condition in order to facilitate vital provider interventions, those living with chronic conditions requiring day-to-day management must also make dozens of additional decisions every day. But initiating provider interventions for all of these would simply not be possible nor even desirable. With diabetes, for example, these can range from food and exercise choices to the need to take medications or interact with a medical device, like a glucose monitor or an insulin pen or pump.

Enter precision engagement. Just as precision medicine can utilize a patient’s genetics or metabolic profile to uniquely fine-tune the dosing of a drug to an individual, precision engagement—with the help of AI and machine learning—can be used by digital health developers and physicians to program connected care platforms to issue electronic interventions or “nudges” that are uniquely tailored and helpful to the individual patient.

These digital nudges prompt a patient to take necessary actions throughout the day that are not only personalized to their needs but delivered in a way that is consistent with their lifestyle and preferences, leading to a better likelihood of engaging the patient and, ultimately, guiding them to better health outcomes. These digital interventions are known in behavioral medicine as just-in-time adaptive interventions or JITAI, and they are helping healthcare professionals use software to precisely engage the right patients with the right interventions at the right time.

With precision engagement, these solutions programmed into connected care platforms are able to digitally “learn” about an individual patient’s preferences from their responses to questions and from the daily decisions they make in their self-management as they engage with the platform’s corresponding app. This learning enables the software to personalize future digital nudges for the patient.

Precision engagement software might be used, then, to help identify the right moment of the day to generate a nudge, like suggesting the patient eat an apple or take a walk at a specific time of day because that’s when the individual is most receptive to such a suggestion.

Or, a digital nudge might involve time- or activity-triggered reminders, such as the need to take medication or to sync the patient’s medical device to the connected-care platform. It might even send the patient an encouraging message prompted by their reaching of a daily target, such as meeting a specific exercise goal.

Precision engagement can even tailor the type of communication used for nudges, from the use of a pop-up message or the suggestion of a video or article to the kind of voice used—maybe through empathy or even humor—to deliver the nudge. 

Precision engagement is one of the most exciting new developments within digital therapeutics, using digital health tools to deliver highly personalized, time-adaptive interventions in ways that lead to positive behavior change, extraordinary patient experiences, and improved health outcomes.

The need for greater RPM awareness is resulting in a measured pace of adoption

While necessity may have forced the issue for care teams during the pandemic regarding the adoption of telemedicine appointments, it turns out that remote patient monitoring (RPM) is still “one component of telehealth that has lagged,” according to the Medical Group Management Association (MGMA). In a Stat poll of 586 healthcare leaders taken by MGMA last year, the association found that 75% of medical practices had yet to offer RPM services.

Despite patients’ positive perspectives of RPM, demonstrated outcomes, payor recognition of RPM’s value, and the establishment of reimbursement mechanisms, the actual pace of RPM adoption has turned out to be more deliberate than these factors had originally led many to predict. In fact, in our work, we’ve found that a large part of preparing providers to make the actual leap to RPM adoption has really been a challenge of growing awareness.

For one thing, we’ve found that in the busy world of providing clinical care, some providers simply haven’t gotten a complete understanding of what RPM reimbursement looks like. So, we continue to chip away at the task of making sure our provider partners have the latest information.

And while some may have caught wind of RPM reimbursement, we’re coming across other providers who have the misconception that only Medicare reimburses for RPM. In reality, there are dozens of private payors covering RPM, with some reimbursing at even higher levels than Medicare. 

Another misconception we encounter among some providers is the mistaken belief that, to get reimbursement for RPM, they must implement every piece of it all at once, from getting patients set up and syncing their data to analyzing the data and providing patient consults. Not only is that not true, but the idea of such a weighty burden is partly why CMS has assigned unique CPT codes for discrete RPM activities. For many providers, implementing RPM is such a significant change management challenge that it actually makes the most sense for them to start small, which they can do by getting patients set up and focusing them on simply sharing their data remotely on a monthly basis. With that, providers can begin submitting for reimbursement, then build from there.

One of the most useful steps for providers unsure of where to begin is to find a reliable partner who specializes in RPM planning and implementation. Resources like AMA’s recently published 12-step RPM Playbook can help, as it covers every stage of establishing a fully operational RPM program.

Pandemic-induced use of decentralized clinical trials provided an up-close view of their efficiencies and is leading to increased adoption

Decentralized clinical trials (DCTs) are trials in which some or all study assessments are conducted at locations other than the investigator site via either tele-visits, mobile or local healthcare providers, local labs and imaging centers, home-delivered investigational products and/or mobile technologies. During the pandemic, when thousands of non-COVID trials—some 80%—were interrupted, virtual trial companies experienced an explosion in demand.

And if market projections are any indicator, demand for DCTs will continue to increase, with an analysis issued earlier this year projecting the global DCT market will grow from $6.1 billion in 2020 to nearly $16.3 billion in 2027.

While the need for social distancing that precipitated the sharp uptick in DCT demand may have subsided from its peak during the pandemic, it’s clear that continued demand for DCTs will be driven primarily by the efficiencies of the model that researchers witnessed first-hand during the pandemic.

One of the biggest advantages of DCTs is how they boost trial enrollment, as they often allow for patients to sign up and participate from home via remote monitoring. Remote participation opens trials and the benefits they provide to those living outside urban centers, which means the trend toward DCTs is also broadening the number and diversity of eligible enrollees.

DCTs can also reduce patient dropout rates and speed up study timelines, two of biggest challenges in life sciences R&D. And they help researchers realize significant cost savings from decreases in the number of physical trial sites and reductions in research staff and travel.

Driven by this wide range of efficiencies benefiting subjects, researchers, and study sponsors, it’s expected the demand for DCTs will continue to ramp this year and in the future.

Overall, we expect 2023 to be a year where our prospecting and development efforts in the connected care landscape will continue producing exciting advancements that will enable us to better support patients living with chronic conditions as well as the physicians and teams who care for them.

About Russ Johannesson

Russ Johannesson is Chief Executive Officer at Glooko, a leading provider of connected care, patient engagement, digital therapeutics, and clinical trial optimization. Deployed in over 30 countries and 8,000 clinical locations, Glooko’s mission is to improve the lives of people with chronic conditions by connecting them with their caregivers and equipping both with digital health technology for improved outcomes.

What You Should Know:

– Phenomix Sciences (Phenomix), a precision medicine biotechnology company that brings data intelligence to the treatment of obesity, announces today the launch of its first therapy selection test, the My Phenome Hungry Gut test.

– This is a first-of-its-kind obesity test and will determine if a patient’s phenotype is Hungry Gut (Satiety) or feeling hungry shortly after eating a meal. If a patient is Hungry Gut phenotyped, providers are able to more precisely and accurately make a treatment plan which would include a diet intervention specific to Hungry Gut, GLP-1 (glucagon-like peptide) medications, and intragastric balloons.    

– The Hungry Gut Test will initially be rolled out to a select group of providers before a full roll-out later this year.

What You Should Know:

• Genialis, a computational precision medicine company unraveling complex biology to find new ways to address disease, today announced it raised more than $13 million in Series A financing to transform the way diseases are diagnosed and treatment decisions are made.
• Taiwania Capital and Debiopharm Innovation Fund co-led the round, with participation from previous investors First Star Ventures, Redalpine Venture Partners, and Pikas. Other new investors include P5 Health Ventures and several Angels. Ita Lu of Taiwania and Hamzeh Abdul-Hadi of Debiopharm will join Genialis’ Board of Directors. 

AI/ML-enabled Platform With a Biology-First Approach

Genialis is developing next-generation patient classifiers using machine learning and high-throughput omics data to capture underlying disease biology and predict how patients will likely respond to targeted therapies. The company will use the funds from the Series A to expand its proprietary ResponderID(™) platform and build out its comprehensive collection of clinically validated biomarker models to provide pinpoint diagnoses for virtually every cancer patient. To date, Genialis has used ResponderID in collaboration with biopharma to analyze clinical trial data and inform future trial designs for numerous investigational drugs. Genialis also supports the commercialization of next-gen biomarker assays with several leading diagnostic firms.

“With ResponderID, we sought to disrupt the historical linear progression of drug discovery and development, rather aiming to close the loop between drug development, patient care and new drug discovery,” says Rafael Rosengarten, Ph.D., co-founder and CEO of Genialis. “We chose to focus initially on biomarkers that improve the efficiency of drug development, that ensure the right patient gets the right medicine, and make an impact on real people’s lives in a shorter period of time.”

ResponderID is a machine learning platform for clinical and translational research, built from years of experience working with partners across the industry and advanced internal R&D. ResponderID yields new biomarkers for drug development and discovery programs, as well as diagnostic tests. ResponderID can read the status of virtually any NGS-based biomarker, including bespoke and proprietary signatures, from a single assay. The resulting output provides clinical and translational researchers with a comprehensive molecular portrait of patient disease phenotype enabling the most informed decision-making possible.

“ResponderID, Genialis’ predictive biomarker platform, enables precision medicine by identifying patients that are most likely to respond to treatments. Its use in drug development will optimize study designs and improve chances of clinical trials success, driving much-needed productivity gains for pharma R&D and accelerating the time to market for promising new drugs,” said Hamzeh Abdul-Hadi, Investment Director at Debiopharm Innovation Fund. 

Last year, ten publications and poster presentations at major scientific conferences featured results generated with ResponderID, including AACR, ESMO, and SITC. Genialis also co-authored a paper in the Journal of Clinical Oncologydescribing OncXerna’s navicixizumab ph1b trial, including retrospective analysis with the Xerna TME Panel. 

“Genialis is leading the collision of biology and AI. Our approach is biology first, but with a deep commitment to getting the data science right. Thus, we only succeed as a team that understands both worlds,” said Miha Stajdohar, Ph.D., co-founder and CTO of Genialis. “This capital brings together a global syndicate of clinical oncology and deep tech experts and will allow us to grow our in-house capabilities in multiple disciplines.”Genialis is growing its teams in both the U.S. and Slovenia across business, operations, life science, and data science functions and expanding its advisory boards. The company is also investing in R&D collaborations with several leading cancer centers, hospital groups, and clinical academic labs. 

What You Should Know:

– Accenture published Fast-Forwarding Healthcare through Data Collaboratives Report to discover how best health ecosystem players access data to fast-forward healthcare.

– Data generated by technical and scientific advances, like connected health devices, consumer health and medical-grade digital apps, individual multi-omics profiling data and the move to cloud storage, holds great promise for accelerated healthcare progress and precision medicine, and data collaboratives (DCs) might be the path forward. This report takes a closer look into DCs with a specific focus on oncology, as one of the largest therapeutic areas in terms of industry spending and new medications’ launch pipelines.

Understanding the Potential Data Collaboratives Hold to Generate Value in Healthcare

Life sciences companies increasingly use DCs to access high-quality data from ecosystem partners. The aim is to accelerate research and early development (R&D), clinical development, and post-launch insight generation. To better understand the extent, implications, and potential benefits of DCs, a survey was conducted  among 59 people who are heads of oncology and institutional review board (IRB) members. Of those surveyed, two-thirds already participate in DCs, and the remainder does not. This quantitative research was supported with qualitative research among 18 cross-industry experts (pharma & MedTech, technology service providers, cancer registries, research institutions, patient communities). The research was conducted across the US and the top five EU countries.

Oncology was deliberately selected for a close look into the therapeutic implications of DCs as it is one of the largest therapeutic areas (TAs)3 in terms of industry spending and new medications’ launch pipelines. The outcomes of the findings could apply across TAs. First, the researchers wanted to understand oncologists’ key DC participation drivers—including their preferred ecosystem setups. Then we profiled the key challenges involved in establishing DCs. Challenges to success were assessed and accelerator were looked at, along with, success factors, and strategies vital to effective DC establishment.

Oncologists believe that data collaboratives generate value\

The overall trend was clear: respondents were unanimous in their view that DCs generate significant value for patient outcomes. Nearly two-thirds (64%) agree strongly and 36% agree somewhat. They also generally believe that DCs generate significant value for research outcomes, though the agreement is more muted on this point (38% strongly agree and 54% somewhat agree). The survey indicates that DCs are more significant for patient outcomes than for research outcomes. Although Pharma and MedTech companies were not surveyed, it would be expected  to see significant DC value in the R&D space. While DC participants currently tend to share data more readily with regulatory authorities and clinical stakeholders, they are open to sharing more data with pharma companies in the future.

Technical and human obstacles to data collaboratives: As far as obstacles go, technology and sponsorship seem to be common threads among all respondents. The top five challenges, in order, are:

– Technical integration.

– Lack of sponsor commitment.

– Compliance with federal data protection regulations.

– Lack of trust among participants.

– Data harmonization

Life sciences companies can’t wait to have an extensive data ecosystem at their fingertips. The implications for product development are immense, and DCs represent a particularly attractive opportunity to stimulate innovation and access large datasets from multiple parties. However, establishing a DC is not to be underestimated as a venture. Many of them fail for lack of a solid business case or because the needs and interests of participating parties are not well understood, preventing the realization of cross- organizational benefits. Some fail at the point of conception, while others are unsustainable in the longer term. Yet, the faster and more effectively companies can implement DCs, the greater the competitive advantage they derive. Ultimately, the pioneering insights they obtain could drive improved healthcare—and make it more sustainable.

The report also highlighted pertinent challenges associated with DC’s which are as follows:

Business Challenges: Business challenges associated with DCs apply to both oncologists participating in DCs and those not doing so. High participation costs are flagged by 40% of those surveyed. Another key issue is that for 25% of the sample group, the benefit of participation is unclear – and the absence of a business model to generate a return on investment makes sponsors reluctant to commit. Single-sponsor dependence is also very risky for a DC project. A sustainable business model will generate value and improve revenues, winning over more than one sponsor. The study reveals that prospective DC partners want to use RWD from the collaborative to identify new treatment targets and biomarkers. Oncologists already using data collaboratives find the greatest value in clinical care improvement and decision-making. This dichotomy of interests suggests that a clear understanding of prospective partner needs is essential to attracting the right DC partners.

Data Challenges: Among oncologists spoken to, a hefty 64% of those who participate in data collaboratives have faced obstacles with the data (insufficient variety). Nearly half (44%) of the academic medical centers said that key required data sets weren’t available from the partners in the collaborative (too much data heterogeneity). Nearly a third (31%) said the DC provided poor- quality data. Other challenges included data heterogeneity (more than half (56%) of respondents said data harmonization is one of the biggest challenges). As a consequence, 21% of respondents highlighted the onerous process of agreeing on a common data model and terminology to reduce heterogeneity. Every potential DC partner follows its own standard or has simply adapted standards to some degree—a finding confirmed by our expert interviews. Disparate standards make it difficult to share data usefully between institutions—which applies mostly to unstructured data but not exclusively so. Overcoming these challenges is vital.

Technical Challenges: Almost all (92%) of academic medical centers in data collaboratives have faced technical issues. The main technology challenges involve the technical integration of hospital systems with the collaborative. A third have faced technical difficulties with the DC platform itself or its associated services. The  survey indicates that more than 90% of academic medical centers still use a purely centralized approach for data exchange with the DC. The centralized approach creates lengthy legal discussions on data transfer. When DCs generate copies of data, adherence to data privacy regulations becomes more complex. The approach inherently prevents expansion into certain countries due to local data privacy and security laws that prohibit data copying or transfer. Further drawbacks of the centralized approach include consent management difficulties, double- data entry, and large data transfer costs.

Data Privacy Challenges: Compliance with data sharing or data protection regulations is important. Data privacy issues are more common in Europe than in the U.S—on average, almost seven in 10 of those we surveyed have faced data privacy/legal obstacles. That number rises to nearly eight in Europe. The experience of setting up data collaboratives and enabling data-sharing initiatives across regions has shown that transferring health data across country borders is also an obstacle. For oncologists not currently participating in a DC, data security and privacy constraints represent the top barrier to participation in a DC. In fact, data privacy and security constraints are a hurdle for 60% of potential partners. addressing and implementing technical data security and privacy requirements was a challenge for 21% of our sample group.The challenge is underlined by the fact that 31% of medical centers want better data privacy and security approach.

Partner Ecosystem Challenges: Appropriate partners are, per definition, indispensable to the success of a DC. Potential partners must be identified, approached, and evaluated against a set of criteria. Among our respondents, almost nine out of ten have faced obstacles with the partner ecosystem. In terms of ecosystem partner challenges, a lack of trust among participants was the fourth most mentioned challenge overall among participating medical centers. It was also one of the challenges most often raised by industry experts. Political conflicts and partners’ personal interests could be related to a lack of trust, and respondents mentioned them as important challenges.

Legal Challenges: Research found that 64% of academic medical centers have faced governance and legal challenges. Challenges include the creation of data transfer, usage, and governance agreements and agreeing on intellectual property (IP) and other contractual terms. Industry experts we spoke to confirmed that contractual and governance agreements and signings are particularly onerous hurdles. Yet it is crucial to create an overarching (and binding) legal structure to achieve mutual trust and transparency.

What You Should Know:

– PathGroup, one of the largest providers of anatomic, clinical, digital, and molecular pathology services in the United States, is expanding its operations with Proscia’s software platform (Concentriq® Dx) to further enhance the delivery of faster, higher quality results for millions of patients, helping to better inform treatment decisions.

– PathGroup currently supports more than 15,000 referring physicians and over 200 hospital customers with diversified laboratory testing services and high-touch customer service.

Concentriq Dx Background

Concentriq Dx is a modern pathology platform that drives routine diagnosis for laboratory networks of all sizes. By streamlining collaboration and enabling remote image viewing, it empowers pathologists to deliver higher quality results through broadened access to expertise. The platform also offers world-class interoperability with leading scanners and image analysis applications and is designed for realizing the promise of pathology’s AI-powered future to meet evolving needs.

Why It Matters

Up to 70% of clinical decisions depend on pathology. Digital pathology shifts diagnosis from microscope to whole slide image, unlocking insights previously unseen by the human eye to realize the promise of precision medicine. It also drives powerful efficiency gains that are helping laboratories to overcome a range of systemic challenges; the number of cancer cases is expected to rise 47% between 2020 and 2040 while only 65% of pathology groups looking to hire could fill all open roles in 2021.

“Taking this next step on our journey requires a company to serve as our trusted partner and a platform that will grow across our network,” said Ben W. Davis, MD, CEO of PathGroup. “After a robust evaluation, we are thrilled that Proscia shares our vision for a fully digital future and confident that Concentriq Dx will enable us to rapidly expand upon our existing digital pathology foundation. With Proscia’s nimbleness and speed to innovation, we will leverage the latest technology, including AI, to provide the next generation of diagnosis for millions of patients.”

What You Should Know:

– Today, the AI-powered genetic testing and precision medicine company Tempus announced that it has signed a significant strategic agreement with Pfizer to more precisely gather insights that will inform novel drug discovery and development in oncology.

– Through this collaboration, Pfizer has access to Tempus’ AI-enabled platform and its library of de-identified, multimodal data to uncover insights that will power therapeutic development in oncology.

– Pfizer also has access to Tempus’ broad range of capabilities that support therapeutic R&D, to advance its own oncology portfolio, including AI-driven companion diagnostic offerings and Tempus’ clinical trial matching program, TIME, that rapidly activates studies for patients in communities across the country.

What You Should Know:

– Tempus, the $10B artificial intelligence and precision medicine company, announced their first multi-omics collaboration with Actuate Therapeutics, in which datasets of different omic groups – genomics, transcriptomics, epigenomics, and others are combined during analysis to improve research and enable new discoveries, including ones that would have been missed with a single method alone.

– This multi-omics approach is being used by Actuate Therapeutics in support of their Phase 1/2 oncology study of elraglusib, a GSK-3β inhibitor, which includes a randomized study in patients with metastatic pancreatic cancer.

– The drug has shown clinical benefit for multiple advanced cancers. Tempus’ diagnostic technology and multimodal (i.e., DNA, RNA, and imaging) data will support the discovery and validation of novel targets and biomarker profiles that will help determine which patients may respond better to the drug – demonstrating a new way in which precision medicine can be incorporated in clinical development.

What You Should Know:

– Over the past few years, Artificial Intelligence (AI), and more specifically, Machine Learning (ML) technology, have experienced rapid adoption in the healthcare space as tools for diagnosis and decision-making. Such tools are intended to address challenges in the healthcare system to both processes and put into practice the proliferating medical findings, and also to support delivery of the promise of personalized and precision medicine.

– Unintended bias in AI and AI-driven healthcare applications is an evolving topic that developers, reviewers, and experts are still learning to address effectively and consistently. The Good Machine Learning Practices Working Team of the AFDO/RAPS Healthcare Products Collaborative believes the topic of bias could benefit from standard taxonomy, consistent approaches to identification, and addressing any identified sources of bias.

Understanding Bias in Artificial Intelligence

This is the third paper from the GMLP Working Team. The team is an industry-led volunteer team that has come together to lift the use, adoption, and implementation of AI through the teams’ collaborative and innovative efforts.

“Unintended bias in AI-enabled systems is a challenge in the development of ML algorithms,” says Pat Baird, working team co-chair and Sr. Regulatory Specialist at Philips.  “The whitepaper outlines a framework for bias opportunity detection, assessment, and mitigation of unintended bias that can be used by product developers.” Leveraging established and proven methods currently used for risk analysis in healthcare systems specifically for unintended bias should enable more robust management, adds Baird. The paper asserts that bias can occur at points in the data supply chain and product supply chain. While focused on product developers, the paper also discusses perspectives from regulatory reviewers or other stakeholders of these solutions.

Currently, medical devices that use AI-enabled algorithms utilize machine learning (ML) as a mechanism to “learn” during the algorithm development process. Being diligent about the identification and mitigation of unintended biases is important as a guard against bias impacting certain patient populations and resulting in inequities in healthcare delivery. Unless bias is circumvented, resulting reports of inequity may lower trust in the output of an AI-enabled medical device and create a barrier to the adoption of ML technology in healthcare.

Unintended bias in AI-enabled healthcare applications (including medical devices) will continue to be an evolving topic that developers, regulators, and experts must continue to address. This paper leverages existing risk management and related processes and proposes a framework for bias opportunity detection, assessment, and mitigation of unintended bias.

Here are the additional key take-aways from the paper regarding identifying and dealing with bias:

Initial Bias Analysis:

Identify the Intended Use of the System: The first step in mitigating bias in AI/ML-based systems is to define the intended use and indications for use of the system against which the impact of unintended bias can be measured. The intended use of a medical device is variously identified as the “use for which a product, process, or service is intended according to the specifications, instructions, and information provided by the manufacturer,” and “… should take into account information such as the intended medical indication, patient population, part of the body or type of tissue interacted with, user profile, use environment, and operating principle” while considering “reasonably foreseeable misuse.” For software systems, a more granular view of intended use and indications for use and an elaboration of the above definitions can be found in a technical report providing guidance for non-device regulated software. The description, however, applies across the spectrum of device and non-device systems. “

Identify Applicable Biases: Accordingly, a first step in determining the presence of unintended bias could be to identify known foreseeable sources and types of bias associated with the intended use of the system.

Estimate the Impact of Bias on Intended Use:  The next step would be to evaluate the impact of unintended bias on the intended use of the system. This could be achieved by:

● Providing a subjective narrative for each bias source/type/sequence combination that details the positive, negative, or neutral impact of the application of unintended bias on the intended use of the system.

● If the bias source/type/sequence combination has an impact on safety (including data privacy), then ensure the combination is considered as part of safety risk analysis.

● Establishing a qualitative scale to categorize each bias source/type/sequence combination incorporating the following elements:

● Likelihood that the bias source/type/sequence combination will occur during real- world use of the system.

● Likelihood that there will be impact to the intended use of the system if the bias source/type/sequence combination occurs.

● Degree (and categorical type) of impact to the intended use of the system if the bias source/type/sequence combination occurs. (Note: A more granular view of this attribute may be provided if there are variable degrees of impact each with a separate likelihood for that impact, given that the combination occurs.

● Scoring based on the qualitative scale of positive, negative, or neutral impact of the bias source/type/sequence combination to the intended use of the system.

● Indicator of safety impact or no safety impact (including data privacy) of the bias source/type/sequence combination.

Evaluate Bias:

Determine Acceptability of Each Bias: Based on the impact estimation above, determine whether each unintended bias source/type/sequence combination is acceptable (requiring no further mitigation) or unacceptable (requiring mitigations be identified and implemented). Consider one of the following methods for this determination:

– If a subjective narrative was used for impact estimation, provide a further narrative rationalizing whether the unintended bias source/type/sequence combination is acceptable or unacceptable.

– If a qualitative or quantitative scale was used for impact estimation, use a series of decision matrices to make the acceptability determination

Mitigate Unintended Bias

Determine Mitigations: For the unintended bias source/type/sequence combinations considered unacceptable, determine (and document in the bias analysis) mitigations (e.g., changes to requirements, architecture, design, data, code/algorithms, and/or algorithm training) necessary to bring bias source/type/sequence combinations to an acceptable state. The goal of determining appropriate mitigations is to reduce the likelihood that the bias source/type/sequence combination will occur, reduce the likelihood the bias source/type/sequence combination will have a negative impact if it occurs, and/or reduce the level of impact of the bias source/type/sequence combination.

Implement/Verify Mitigations: Where mitigations have been identified, implement them in the system using controlled design change and configuration management, where applicable. Verify implemented mitigations through both static and dynamic means (e.g., requirement reviews, technical reviews, code reviews, static code analysis, unit testing, integration testing, functional system testing).

Determine Residual Impact of Bias on Intended Use: Using the methods defined above to estimate and evaluate bias, determine the residual impact of unintended bias to the intended use of the system after bias mitigation mechanisms have been implemented and verified.

Determine Potential Bias Arising from Mitigations: Perform an estimation and evaluation of potential new or changed unintended bias source/type/sequence combinations that may arise from the implementation of bias mitigations. Where applicable, identify, implement, and verify mitigations on these new or changed bias source/type/sequence combinations.

Determine Completeness of Mitigations: Review all bias mitigation activities to ensure that the impacts from all identified bias source/type/sequence combinations have been considered and that all bias mitigation activities are complete.

Evaluate Overall Bias:

Determine Overall Acceptability of Bias in the Systems: Taking into consideration the residual impacts of all bias source/type/sequence combinations, determine whether the overall residual impact of bias source/type/sequence combinations is acceptable or unacceptable. If the overall impact is considered unacceptable, consider identifying and implementing further bias mitigations, making changes to intended use, identifying and documenting an acceptable benefit vs. bias assessment (refer to next paragraph), or reconsidering whether to release the system in its current configuration.

Articulate Benefit vs. Bias: For unintended bias source/type/sequence combinations that remain unacceptable, gather and review data to determine if the benefits of the intended use of the system (e.g., technical, clinical, economic) outweigh the residual impact for this specific combination. Consider also making an overall benefit vs. bias determination at the system level. If the benefits do not outweigh the residual impact, consider further system design changes (including bias mitigations and/or changes to intended use), or reconsidering whether to release the system in its current configuration.

Disclose Residual Bias: Disclose known residual bias source/type/sequence combinations including their predicted impact on the intended use of the system to relevant internal and external stakeholders (e.g., company management, customers, users, regulatory authorities). This may be done via a formal report, release notes, instructions for use, or other applicable communication mechanisms. In support of transparency, clear disclosure of intended bias needs to be disclosed as well

The healthcare industry is in the middle of deep digital transformation across every aspect of the patient journey. Technology has always been important in healthcare, but COVID-19 has accelerated advancements, improving the way care is delivered. The pandemic also increased patient interest in being more involved in their healthcare decisions. And when patients are more involved, they are typically more satisfied with their experience and have better health outcomes. From remote patient monitoring technology that allows patients to track biometric data at home and report that information to their doctors to precision medicine that is tailoring healthcare to a patient’s specific genetic, environmental and lifestyle factors, technology is proving to improve the patient experience. 

Advanced technologies have also made their way into the surgical suite. These “smart ORs” contain next-generation physical equipment connected by software integrations that communicate with each other around the procedure. The benefit of leveraging these technologies to improve every part of procedural processes is undeniable. As surgical and interventional suites become even more advanced, the adoption of new technologies will also accelerate. The result? A reduction in administrative burden will allow clinicians to focus on what’s most important: treating the patient and delivering the best outcomes. 

Here are five things all smart ORs should have or do to optimize efficiency and patient outcomes. 

1. Live Streaming Compatibility 

In a smart OR, not only is the entire room connected from within – it should also be able to connect those in the room to key opinion leaders (KOLs) or trainees that are in other locations to facilitate better education and collaboration. This is achieved through live streaming. Medical device representatives, clinical specialists and physician fellows, among others, began embracing this technology in the OR prior to 2020, but the pandemic made this feature an imperative in hospitals across the country. The ability to connect with more people supporting care delivery and sharing expertise is paramount to helping decrease costly and potentially harmful procedural variability and improving patient outcomes across the board. 

For physicians who are learning and training on a new procedure, live-streamed cases provide them with a front-row seat in real-time, no matter where they are in the world. Previously, these fellows would have to take time away from their practices and travel to wherever the expert surgeon they needed to train with worked. This keeps physicians away from their own practices resulting in lost revenue, patients going untreated and a general interruption to physicians’ personal lives. Additionally, these fellows are often asked to stand in the back of the procedure room with a limited view of how the surgeon is performing the procedure. With smart OR technology like digital case support, physician trainees can join a live stream of a procedure and follow along with a guided, intraprocedural playbook of best practices. This digital approach allows trainees to better engage with what is happening in the OR, including two-way communication with proctors to ask questions and gain deeper insights throughout.

For medical device representatives, live streaming curbs the need for constant travel between the hospitals they work with, reducing the cost and complexity of adopting new procedures. The ability to be in more places in less time allows device representatives to continue sharing best practices and elevating their support to physicians and care teams so that clinicians can increase efficiency and reduce case variability. Across these use cases, high-fidelity, easy-to-use live streaming is better for surgical training and product adoption, enabling learning and collaboration that democratizes access to the latest care which ultimately benefits patients. 

2. Improve Surgical Teams’ Workflow  

Prior to the start of surgery, and even during the procedure itself, the OR can be a chaotic environment as clinicians and medical device representatives prepare for the forthcoming procedure. Smart OR tools and technologies are meant to help optimize patient care by creating a more efficient and effective surgical workflow that reduces procedural variability. Imagine you are a new scrub nurse assisting on your first procedure. If there was a technology that could allow you to familiarize yourself with best practices and the procedural workflow in a digital format prior to the surgery and help ensure you pulled all the required surgical instruments in advance, you would have greater confidence heading into the procedure. Not only were you able to gather the supplies in a timely fashion, but you also know when to hand each tool to the surgeon during the procedure, helping clinicians in their quest to deliver the highest quality of care and, in turn, improve patient outcomes. 

3. Automated Administrative Tasks

In a smart OR, all administrative tasks should be automated so that the only pre-op, intra-op, or post-op work performed by the team is meant to accomplish one goal: achieving the best outcome for the patient. This is especially true as it relates to surgical workflows, which are still made up of mostly manual tasks and undocumented, institutionalized knowledge.

One area that benefits from automation in the OR is the surgical supply chain. Point-of-use supply chain management solutions provide clinicians with automation tools so they can spend less time managing inventory and more time caring for patients. This makes a scrub nurse’s job easier when they gather supplies from the OR storage room before a procedure. When a medical representative arrives with the device needed for the operation, the nurse can scan the device with a point-of-use scanner, which will make sure that the device’s unique device identification (UDI) gets into the hospital’s EHR and back office so that a requisition and purchase order can be created. This advancement means the provider team does not need to track down the representative after the operation.

Additionally, automated PHI masking functionality that harnesses artificial intelligence offers another opportunity. This technology not only helps conceal protected health information (PHI) during a live stream of a procedure to protect the patient’s information in real-time, but it also enables medical device organizations to avoid the post-procedure task of redacting PHI via video editing software when using the live streamed procedure for training, education or commercial initiatives.  

4. Easy Set-Up and Understanding of Tools 

For smart ORs to deliver on the value of improving surgical workflow and reducing procedural variability, the tools and technology must be easy to access and use for all stakeholders. If the setup is complex or the learning curve steep, surgical team members may be more resistant to adding those additional steps into their already-busy pre-op routines, and overall adoption of the smart technology will be slower. Adding technology to smart ORs almost always means adding something new to an existing workflow, so the more familiar the experience is to a user, the smoother the adoption will be. 

Additionally, the less space smart technology occupies the better. ORs are small compared to the amount of equipment and number of people that must have access to perform a procedure. Tools, like digital case support technology, that can reduce the number of non-essential personnel in the OR by remotely live-streaming cases reduce the risk of contamination in the sterile field and leaves more room for the surgical team to safely operate. It also provides a better vantage point for those who are intending to observe the procedure, further contributing to a higher quality training experience. 

5. Complementary Technology 

Technology in the OR should enable clinicians to work smarter, not harder. As previously mentioned, if the setup is too complex, surgical team members will be more resistant to adopting those additional steps into their already-busy pre-op routines. The same goes for technological interoperability. When smart OR hardware and software is built for compatibility, while not presenting friction for the end-user, all stakeholders in the OR – from surgeons to scrub nurses to the patient – win. Cloud-based and device-agnostic technologies, particularly software-as-a-service (SaaS) platforms that offer secure, remote access via smartphones and tablets, are also preferred. 

Digital case support platforms are a good example of this as they are most effectively used in conjunction with other smart OR tools such as robotic technology. Dr. Vasili Karas, an orthopedic surgeon in the Chicago area, believes that robotic-assisted total joint procedures are the way of the future for patients who qualify. Yet this new technology can introduce new, unfamiliar steps to an already complex workflow. By leveraging digital case support during his procedures that use robotic assistance, Dr. Karas can help ensure his team is on the same step at the same time, working in unison. This smart technology is also helping him train other physicians interested in onboarding innovative robotic orthopedic technology in their own practices.

The benefits of a smart OR are endless and the technology powering them continues to get smarter. As digital health transformation continues to play a growing role in areas like clinical workflows, data collection, patient care and more, healthcare providers and suppliers have an opportunity to work together to ensure the OR is a safe, functional and efficient environment to achieve better patient outcomes. 

About Ryan Padilla

Ryan Padilla is the Executive Director at Explorer, a GHX company. He is an accomplished leader with 20 years of consulting and industry experience solving challenges in corporate strategy and business process optimization.

Clinical trials should be a time of promise for better patient outcomes, as they explore new ways to potentially help patients suffering from a variety of conditions. Instead, this phase of treatment development is often met with exasperation as patients, researchers, and drug developers deal with the many barriers to clinical trial recruitment and access.  

There are two central – and intrinsically linked – problems with the current state of clinical trial recruitment in the U.S.: the inability to recruit enough patients, and the trials’ failure to reflect the diversity of our nation. Due to a lack of intentionality in relation to the inclusion of populations who may be under-represented, clinical trials may have been historically biased toward those who were an active part of the medical system and were not averse to becoming trial participants. While people of color make up about 39 percent of the U.S. population, these groups represent from 2 to 16 percent of patients in trials. In addition, a study published in JAMA Oncology found that patients with an annual household income lower than $50,000 were less likely to participate in clinical trials than those with higher incomes. Everyone experiences the disease differently, and it’s critical to ensure clinical trials include individuals with demographic variability, differences in socio-economic status (SES), and lived experiences. When one population is overrepresented in clinical trials, that community benefits more significantly from scientific advancements and developments. 

To that effect, we must examine the issue of clinical trial recruitment with a nuanced perspective to understand how a multitude of factors lead to a lack of diversity in clinical trials – and what can be done to change this.  

Across the nation, clinical trials are critically under-enrolled 

Currently, 80 percent of clinical trials are under-enrolled, and it’s estimated that more than 30 percent of patients who join clinical trials drop out. Conducting clinical trials is an uphill battle for physicians, biopharma companies, and patients alike. To attract more patients to clinical trials and break down barriers to retention, we must first understand why participants choose to not participate, and why, even after they’re enrolled, patients fail to complete them.  

Increasing public awareness of clinical trials 

In recent years, there has been a push to increase public awareness of clinical trials. Health literacy is one of the biggest barriers to trial awareness – many people are not aware of what clinical trials involve or why they matter. While the lack of health literacy in our country alludes to a larger problem within our healthcare system, it is also up to practitioners to instill awareness in their patients.  

Patients are usually uninformed regarding their clinical research eligibility or how trials can provide viable treatment options. Because of this, healthcare organizations, those recruiting for trials, and the wider biomedical community need to give providers talking points and materials to better inform their patients. Oftentimes, healthcare providers themselves may be unaware of patient options in clinical research. 

Bringing humanity to clinical trials requires creativity – and technology  

Clinical trial protocols are complex. There are many factors that providers must consider when connecting a patient to a trial. For example, a patient with a gastrointestinal disease like Crohn’s disease, may share that they are experiencing stomach pain, diarrhea, and fatigue with their doctor. Given the patient’s previous experience with approved medications, the patient’s doctor might want to connect them with a clinical trial. To determine whether the patient is a good fit for the trial, the doctor requires a wealth of knowledge about the patient and their medical history, as well as an understanding of the eligibility criteria of various trials.  

Speeding up the process of identifying patients that meet eligibility criteria requires creativity and innovation. Many healthcare providers and biopharma companies are turning to aggregation and curation of the electronic health record (EHR) and automated disease severity scoring with technology like artificial intelligence, which uses an algorithm to help identify the patients that are best suited to clinical trials. This technology can be the bridge that makes the pre-screening process for clinical trials easier and gets patients in the door for potentially life-changing clinical trials. 

Patient retention and increasing diversity in clinical trials 

Increasing clinical trial diversity comes down to increasing patient centricity. Once patients are randomized to the appropriate clinical trial, there is still a chance that they do not complete the study. The 30 percent drop-out rate for patients is a hindrance to bringing potentially life-changing treatments to market. As healthcare professionals, we should work to create a more patient-centered experience for our trial participants so we can continue to bring a better quality of life to patients of all backgrounds.  

Patients drop out of clinical trials for several reasons. Studies have shown that patients do not finish clinical trials due to economic burden, transportation challenges, job changes, lack of appropriate childcare, and a general lack of continued enthusiasm from the patients themselves. In order to increase participation in clinical trials, these trials must be conducted with humanity at their core – especially for those who are socio-economically challenged and those from historically marginalized populations.  

The realization that access to clinical trials is a challenge for many patients is at the core of the increasing movement to decentralize. In a decentralized clinical trial model, the question becomes, how do we bring a trial to a patient rather than expecting them to come to us? Technology has advanced the ability to decentralize clinical trials through electronic consent (e-consent) and virtual visits, but decentralization still requires the human element. At the core of any medical interaction, we shouldn’t lose sight of the ability to hold a patient’s hand at the bedside.

Some study participants have reported that clinical trials lack a human element, and when patients have expressed concerns or asked questions, they have felt more like test subjects rather than patients. This feeling can exacerbate the earned mistrust that historically marginalized people already have in our healthcare system and further contribute to the lack of diversity in clinical trials. As the FDA articulated in their latest diversity enrollment plans, it’s important for the research community to recognize the history of studies, such as the Tuskegee experiments, that generated mistrust in the healthcare system. 

A way forward 

By understanding patient centricity, utilizing innovative automated recruiting tools, adopting a decentralized approach, and creating a human-centered trial design, as researchers and physicians we can ensure that we properly support all patients and increase access to clinical trials. Utilizing humanity and increasing community engagement will allow patients to become more comfortable with participating in clinical trials, which in turn will create more diverse trials and increased health equity.

About Dr. Chris Fourment

Dr. Chris Fourment, Senior Vice President of Clinical Strategy at Iterative Health, a company pioneering the use of artificial intelligence-based precision medicine in gastroenterology (GI), with the aim of helping to optimize clinical trials investigating the treatment of inflammatory bowel disease (IBD). Dr. Fourment has dedicated his career to the study of IBD and clinical trials. He is a member of the American College of Gastroenterology (ACG) and has served on the Crohn’s & Colitis Foundation’s Chapter Medical Advisory Committee. As CEO of CRSG/Precision, he has established the research process for numerous sites across the country.

Tracy Curley, CFO and interim CEO at iSpecimen

Focusing on the macroeconomic environment, which remains impacted by the lingering COVID-19 pandemic, there continues to be uncertainty about the strength of the global, Asia Pacific, UK and US economies. High-interest rates and a potential recession remain a concern for all market participants. At ISPC, we are closely monitoring the pace of specimen transactions. We believe that this industry can be resilient through a continued economic downturn or recession, as well as any impacts from inflation.

Dr. Linda Marban, CEO at Capricor Therapeutics

For the first time in a long time, we are seeing the emergence of three new viral infections: RSV, the flu, and COVID – all of these viral syndromes that were sitting dormant while the world was locked down for COVID-19. Given this triple threat, I think that we will begin to see the industry addressing how we are going to manage infectious diseases moving forward. For a while, things were focused solely on COVID.

Ariel Katz, CEO & Co-Founder at H1

Drug approvals and development will hinge on diversity. The FDA and other governing bodies will increasingly hold pharma companies accountable for diversity in clinical trials. In doing so, we’ll see more and more drugs rejected – not because of efficacy issues, but because diverse patient populations and providers are not being considered or recruited. We’ve already seen this with Eli Lilly and Biogen, and there will be more. This will cost pharma companies millions of dollars in wasted clinical trial costs, to the tune of an average of $1M per day for three years. But it’s extremely necessary and overdue. For progress to take place, companies will need to be held accountable for real, substantial changes to their clinical trial processes.

Dr. Mike Montalto, Chief Scientific Officer at PathAI

During clinical trials, it’s essential to be able to gather as much accurate data related to the patient and to candidate drug’s effect following treatment, such that important decisions can be made as early as possible in the clinical drug development process. Do they have the right patients enrolled who are most likely to respond? Can they see changes locally in the tumor microenvironment that indicate the drug is having a biology effect? Is the drug effect meaningful beyond the measurement noise of endpoint analysis? AI-powered pathology holds the key to answering those and other questions and will be a “must have” data platform for generating entirely new insights from patient samples so drug developers can have confidence they have selected the right patients and can assess sooner whether a drug works. This will accelerate drug development and help get the right therapies to the right patients at the right time, thus advancing precision medicine.

David Bleakman, President of Drug Discovery & Development at PsychoGenics

Necessary and opportunist types of pharma M&A – As many companies struggle to raise money in public markets, necessary M&A amongst weak players that temporarily delays the inevitable and opportunistic M&A where the strong capitalize on distress to pick up assets cheaply.

Marie Lamont, Global RWE Data Strategy, Access & Enablement at IQVIA and General Manager at Inteliquet

While decentralized trials are opening the doors for broader patient populations to be involved in research, there is still room for improvement to reach all groups. Many research studies are focused on academic centers, thus we need to expand to offer more trials into other care settings to ensure better diversification. In the coming years, and especially as AI and automation technologies streamline the trial processes for better efficiency, we will see researchers working with community healthcare centers and professionals to reach underrepresented populations.

Jane Myles, V.P. of Clinical Trial Innovation at Curebase

The future of decentralized clinical trials (DCTs) will become clearer as the industry evolves and as governing bodies clarify regulations around the globe.  Furthermore, the release of ICH E6 will distill many aspects of data expectations and standards. And large scale commercial players like Walgreens, Walmart, and Best Buy entering the clinical landscape will quickly evolve how trials are conducted. At the same time, patients will continue to seek trial options with functions like online data entry, telehealth, and utilizing local providers. The complexity of the evolving modern trial landscape, combined with prioritizing patient needs, means that a one-size-fits-all approach is no longer possible.

What You Should Know: 

–  New company Velsera was announced at the J.P. Morgan Healthcare Conference supported by thematic-focused impact fund Summa Equity (“Summa”).

– Velsera sets out to amplify the impact of clinicians, researchers and scientists for the benefit of patients around the world. Velsera creates a software platform out of science, technology, and informatics, making data actionable, accelerating the pace and potential of multi-omics.

– Velsera, headquartered in Boston, will be led by CEO Gavin Nichols. Gavin was most recently CEO of the global Medical Imaging and eClincial company Calyx, a spinout from Parexel.

New company advances precision health through data-driven solutions

The company enables the democratization of omic data across clinical and research applications, connecting healthcare and life sciences to reveal the true promise of precision medicine – a continuous flow of knowledge among researchers, scientists and clinicians around the world, creating insights that radically improve human health. Velsera’s expansion should be expected in 2023 and beyond.

Velsera transforms science, technology, and informatics into an ecosystem of insight, making data actionable through the integration of a rich software platform, deep domain expertise, and knowledge that accelerate the pace and potential of multi-omics. Velsera sets out to amplify the impact of clinicians, researchers and scientists for the benefit of patients around the world. 

Velsera’s initial formation comes with  the acquisition of three global, industry-leading companies in the healthcare and life science industries: Pierian, Seven Bridges, and UgenTec. Velsera unites these companies to advance and bring together their missions which are centered around improving health globally through multi-omics and insights. The integrated business will remain actively engaged with existing customers, enhance current offerings, accelerate new offerings, and bring integrated solutions to market as the leading provider of global omics and insights.

– Pierian (www.pieriandx.com) – Based in St. Louis, MO, Pierian is a global leader in clinical genomics technology and services supporting a network of laboratories around the globe. Pierian curates the world’s genetic knowledge and offers sophisticated analysis tools to allow for rapid, concise clinical reporting. Its advanced interpretation technology uses adaptive learning algorithms to connect diverse sources of information through machine learning to ensure results are comprehensive and up to date. 

– Seven Bridges (www.sevenbridges.com) – Boston, MA-based Seven Bridges enables researchers to extract meaningful insights from multi-omic, phenotypic and other high throughput data modalities. The Seven Bridges ecosystem consists of a scalable, secure multi-cloud analytic platform, petabytes of connected biomedical data and expert on-demand professional services.

– UgenTec (www.ugentec.com) – Belgian-founded (with U.S. offices) UgenTec brings sample flow intelligence to labs, assay manufacturers and instrument partners to advance modern molecular diagnostics across routine and research applications. UgenTec software and AI solutions deliver workflow automation, testing result interpretation at scale and real-time insights for the digital, connected lab. UgenTec specialties include lab automation, PCR data analysis and clinical-grade software solutions.

What You Should Know:

– AWS announced the launch of a new service, Amazon Omics, to help bioinformaticians, researchers, and scientists store, query, and analyze genomic, transcriptomic, and other omics data and generate insights to improve health and advance scientific discoveries.

 – The explosion of “omics” data, such as genomic, transcriptomic, and proteomic data, is driving a new understanding of biology at the molecular level. This, in combination with clinical information, is being used in drug discovery, vaccine development, and to predict genetic predisposition to disease. But the size, rapid accumulation, complexity, and heterogeneity of omics data pose difficulties.

Amazon Omics Key Benefits

Amazon Omics supports large-scale analysis and collaborative research, without customers needing to worry about provisioning the underlying infrastructure. It enables customers to reduce time spent on setting up and running complex Extract-Transform-Load (ETL) pipelines by natively storing data in optimized query-ready formats (for example, Apache Parquet) with just a few API calls.

Customers can bring their own bioinformatics workflows and Amazon Omics manages the infrastructure to run it. This further reduces undifferentiated heavy lifting, enables customers to operate in a secure environment with built-in access control, logging and audit trails, while still complying with HIPAA, GDPR, and other regulations.

Amazon Omics enables customers to import and easily combine your own data with other publicly available reference datasets in the Registry of Open Data on AWS, such as the 1000 Genomes Project that can be used as a control to understand disease risk; the Genome Aggregation Database (gnomAD) to bring in population allele frequencies to unlock the door to disease detection; and more than 60 other genomic datasets.

Amazon Omics 3 Components

Amazon Omics provides customers with three components:

– Omics-aware object storage to store, discover, and share raw sequence data efficiently, securely, and at low cost.

– Omics Workflows, which allows customers to run reproducible bioinformatics workflows to process raw sequence data at scale either in the Omics Storage or in S3, removing all the undifferentiated heavy lifting associated with running these workflows.

– Omics Analytics, which simplifies analytics through query-ready variants (or mutations) and annotations. While these components will often be used together, customers can also leverage them in a standalone manner.

Why It Matters

 Amazon Omics enables end-to-end omics storage, processing, and analysis by removing the need for organizations to setup and maintain specialized tools, workflows, and infrastructure. By natively integrating with analytics services like AWS Lake Formation and Amazon Athena, Amazon Omics enables customers to maintain management and governance over their omics data that is part of their multi-modal data lake. With a few API calls, customers can deploy a reproducible, production-grade infrastructure to accelerate innovation and time to derive medical insights.