London, United Kingdom (January 24th, 2024) – Concr researchers have adopted and evolved advanced methodologies used by cosmologists to accurately model therapeutic responses in oncology. In a study available as a pre-publication on medRxiv, authors present a novel approach that utilises sophisticated Bayesian statistics commonly employed by astrophysicists to measure the mass of dark matter, to predict the response of therapeutics preclinically and clinically, validated through comparison to historical clinical trials.
Oncology therapeutic development has long been plagued by high failure rates, resulting in substantial and rising R&D costs (median for an oncology drug is currently over $4bn), with only incremental improvements in overall patient benefit and survival. However, recent technological advancements, including molecular cancer characterisation and increased computational power, offer new avenues for addressing these challenges.
The development team, led by Concr’s CTO Dr Matthew Griffiths, employed a pioneering approach to construct "Digital Twins" of individual cancer patients using diverse data. This innovative method allows for the prediction of therapeutic responses to standard-of-care treatments with high precision.
One of the key strengths of this approach lies in its ability to predict the outcomes of clinical trials accurately. In this study, the team validated their methodology by successfully predicting the results of various clinical trials (negative and positive), demonstrating the potential of this approach to enhance and de-risk clinical development of oncology therapeutics.
Dr Uzma Asghar, Concr’s CSO and the senior author, commented: "Drug development for cancer is long, expensive and extremely competitive, with rapid changes in standard of care often seen before clinical studies have completed. Also multiple compounds can be competing for similar clinical indications. This technological breakthrough enables simulations of virtual trial arms, including control arms, and allows investigators to run numerous simulations of drug comparisons, which could boost confidence in study success and provide a competitive edge, as well as spare patients from unnecessary toxicity and limited benefit."
The full version of this paper is expected to be released later this year.
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Concr is a mission-driven techbio company that uses established methods from astrophysics to accurately predict patient outcomes and treatment response to novel and existing cancer therapies. Concr’s unique approach allows iterative learning between disparate and fragmented data across all stages of a drug’s journey into the clinic, removing the need for ‘big data’ and yielding most accurate multi-modal tumour models. Using Concr’s cloud native platform – FarrSight® – researchers can computationally simulate clinical trials, make advanced predictions about biomarkers, and perform standard bioinformatics analyses directly.
Concr is headquartered in London, with a wholly-owned subsidiary in Brisbane, Australia. The company is a venture capital-backed enterprise, with investors including the University of Cambridge Enterprise, R42 Group, Oncology Ventures, Debiopharm, Cambridge Angels, Parkwalk Advisors, Deep Science Ventures and SyndicateRoom.