Transforming clinical trials with open-source tech

open-source data

Clinical trials historically struggled with several key inefficiencies, many of which became painfully apparent during the COVID-19 pandemic, such as the traditional paper-based, manual reviews and analyses. The pandemic also showed how rapidly patients can enrol for therapies with broad public support and clinical applicability. However, finding enough clinical trial participants for most trials, particularly for treatments of rare diseases, continues to represent a significant hurdle.

Similarly, the COVID-19 pandemic demonstrated how technology can enable clinical trial efficiencies, such as telemedicine, patient communication, trial enrolment, etc. Excitingly, innovators in the space are looking to open-source software to overcome legacy inefficiencies and address modern biopharmaceutical needs.

Accelerating timelines for the discovery of novel therapies

Drug discovery is a particularly time-consuming process – not to mention expensive. The pre-clinical stage alone can take three to six years. Open-source tools can be invaluable in accelerating clinical trial timelines by fast-tracking the discovery of new therapies, repurposing treatments already on the market, empowering community-driven support between biopharmaceutical and medical device companies, and revamping trial design and participation with a patient-centric focus.

An early example of open-source solutions helping academics and the biopharmaceutical industry fast-track the discovery of new therapies is the Open Drug Discovery Toolkit (ODDT), a modular and comprehensive toolkit for use in cheminformatics, molecular modelling, and other areas. Written in Python and making extensive use of NumPy (Numerical Python)/SciPy (Scientific Python), ODDT aims to satisfy the need for comprehensive and open source drug discovery software.

Another open-source technology supporting clinical research and streamlining the discovery of new cures for diseases in commercial and academic applications is ACUITY, released by the Digital Experimental Cancer Medicine Team (ECMT) through the Cancer Research UK UpSMART Accelerator Consortium. As an open-source tool, ACUITY seamlessly exports and imports data from multiple sources faster, organises it in one place, and integrates it into existing diagnostic and decision support systems for clinical trials. By providing a more holistic view of relevant data, researchers can use near real-time data to guide resources to trials that offer more promising results and potentially suspend challenging ones. Another benefit of ACUITY is that researchers can reduce costs associated with proprietary software licenses. Such cost savings can be redirected towards research efforts, allowing for more extensive and impactful studies. Lastly, because ACUITY is an open-source tool, its potential will only grow as a foundation for other clinical applications; in fact, ACUITY is currently used in 10 clinical trials, with more in the pipeline.

Open-source software and tools can likewise accelerate the discovery of new therapies through repurposing New Molecular Entities (NMEs) or currently approved drugs. This was the case with Compound S, a reformulation of AZT or azidothymidine, which was developed to fight cancer, but was discovered to have the ability to block HIV activity. Another example is The Institute for Systems Analysis and Computer Science (IASI), also known as Istituto di Analisi dei Sistemi e Informatica, a part of the National Research Council of Italy (CNR). IASI is focused on establishing a network-based tool for drug repurposing. Their tool will be freely available as an R-code and is called SAveRUNNER. SAveRUNNER is a framework to detect putative novel indications for currently marketed drugs against diseases of interest. Other examples of open-source tools helping to facilitate drug repurposing include Insilico, which just received validation of its AI tool PandaOmics, Iktos, which focuses on AI-based generative chemistry for global bio-pharma collaborators, and Roivant, whose focus is on reinventing the development and commercialisation of new medicines through its subsidiaries, called Vants.

Open source, artificial intelligence (AI) and machine learning (ML)-based research efforts, such as AlphaFold, are also accelerating therapeutic discoveries. AlphaFold is an AI system built by DeepMind, which is owned by Google and can predict protein structures. In 2021, it predicted the protein structures for 330,000 proteins and all 20,000 proteins in the human genome. Today, the AlphaFold Protein Structure Database has over 200 million proteins and is available on GitHub.

Deep learning technology (a subset of ML) and its ability to mimic the medical chemist’s pattern recognition process in drug design can also automate drug discovery. A prominent machine learning toolkit utilising this pattern recognition capability is an open source software library for numerical computation called TensorFlow. This software library enjoys widespread use for cheminformatics and bioinformatics modelling.

Open-source promotes data sharing for enhanced collaboration

Just as the pandemic exposed various inefficiencies with clinical trials, so did it highlight the need for effective interoperability and connectivity within the clinical trial ecosystem. Open-source solutions can enable this necessary collaboration, empowering biopharmaceutical companies to accelerate drug discovery through protected data sharing. Data sharing through open-source software can also help research organisations expedite successful model building for automated drug discovery. Similarly, modernising how data flows across the clinical trials ecosystem will enhance the development of treatments and vaccines. The robust community-driven support open-source tools permit will continue to empower researchers to seamlessly share and build on top of each other’s work in unprecedented ways.

Additional ways open-source technology can transform clinical trials

Beyond enhancing collaboration and accelerating timelines for improved drug discovery, open-source technologies can transform clinical trials in other ways. For instance, they can assist with clinical trial design and execution. Likewise, when dealing with rare diseases, open-source tools can empower clinicians to develop new ways to identify, attract, and retain diverse trial participants, benefitting patients and their health outcomes. For example, Casgevy, the pioneering cell-based gene therapy approved for sickle cell disease that affects about 100,000 Americans, only had 44 patients participating in the clinical trial. Open-source technologies have a tremendous opportunity to help establish a new blueprint for identifying and engaging patients suffering from rare diseases in clinical trials. For example, open-source solutions leveraging data analytics, social media platforms, and patient advocacy networks should allow researchers to reach broader audiences.

Open-source tools can also enable the creation of personalised medicines and treatments based on patient-specific needs and symptoms. Digital ECMT’s AQUITY system facilitates a patient’s active participation in digital trials via its PROACT (Patient Reported Outcomes About Clinical Tolerability) solution. PROACT allows patients to share their experiences during a trial via video, audio, or text. These patient-reported opinions provide a glimpse into how each patient lives with their disease and how they manage a treatment regime within their daily activities of living, potentially providing unique insights into treatment management and/or new treatment regimens.

Open-source tools and technologies can revolutionise clinical trials by accelerating the identification of promising therapies and enhancing patient experience with real-time insights into the effects of potential treatments. Open-source technologies can also improve accessibility to trial data, potentially improving insights and shortening the timeline of clinical trials, all in service of improving health and health outcomes for patients.

About the authors

Brian WilliamsBrian Williams works in life sciences strategy & innovation at EPAM Systems, Inc. He focuses on accelerating growth and sharpening competitive differentiation by linking technology and digital innovation to effective commercial strategies. His 20+ years of life sciences industry experience is based on his entrepreneurial efforts, which resulted in the successful sale of two companies and his years with BoozAllen, now known as PwC/Strategy&. At PwC/Strategy&, he led global client engagements for medical device, diagnostic, and biopharmaceutical manufacturers. Williams also co-founded a unique practice group that worked across the healthcare ecosystem and with new entrants, facilitating market entry and novel, digitally enabled commercial strategies. Most recently, he served as Cognizant's life sciences business' chief digital officer, strategy, and consulting leader. Williams is active in industry forums, including serving as a presenter and committee member at HIMSS, AdvaMed, and EucoMed. His articles on the industry’s evolution have been published in InVivo, Economist Intelligence, Forbes, and Strategy&Business. In 2021, Williams was recognised as the “Top Healthcare Consultant” in the US by Consulting Report.

George LitosGeorge Litos is managing principal of life sciences business consulting at EPAM Systems, Inc. With more than 20 years’ experience in management consulting, he has spent the majority of his time in the life sciences space, with expertise in R&D and commercialisation. Litos currently leads EPAM’s offerings in commercial life sciences and is responsible for developing new capabilities that meet the changing dynamics of the industry. Prior to joining EPAM, Litos started his career at Andersen Consulting and was a co-founder of Adjility Consulting (now part of Eversana), where he define new products and services for commercial life sciences clients.