Causal inference
modeling
at scale

Drug discovery, repurposing & validation

Causal modeling is a proven method to ensure success in clinical trials. Over 40,000 citations demonstrate the validity of and demand for this methodology.


Clinical Validation

Real time analysis predicts COVID-19 drug trial outcomes with perfect accuracy

  • Realtime prospective analysis: During the pandemic, we predicted the approval of each new Drug for severe illness due to COVID19 with perfect accuracy.
  • The pandemic allowed for a short turnaround between start of trial and clinical approval or failure by the FDA.
  • For each new trial that was started, we build causal models and predicted approval.
  • Our predictions were flawless and even allowed for the discovery of a new treatment, CDK6 inhibition, which we successfully tested in-vitro.
Disease Drug Target Gene Mechanism biotx.ai Prediction Clinical Trial Result
Severe COVID-19 Interleukin 1 (IL1) Repurposing potential of Anakinra in severe COVID-19 success Phase III trial; approved
Severe COVID-19 Interleukin 6 receptor (IL6R) Repurposing potential of Tocilizumab in severe COVID-19 success Phase III trial; approved
Severe COVID-19 Glucocorticoid Receptor (NR3C1) Repurposing potential of Dexamethasone in severe COVID-19 success Phase III trial; approved
Severe COVID-19 Januskinase 1/2 (JAK1/2) Repurposing potential of Baricitinib in severe COVID-19 success Phase III trial; approved
Severe COVID-19 Cycline dependent kinase 6 (CDK6) Repurposing potential of Palbociclib in severe COVID-19 success Clinical case report; in vitro confirmation
Severe COVID-19 Tubulin (TBB4B) Repurposing potential of Colchicine and Sabizabulin in severe COVID-19 failure Failed Phase II/III
Severe COVID-19 Interferon beta 1 (IFN1B) Repurposing potential of Interferon beta in severe COVID-19 failure Failed Phase II/III
Severe COVID-19 Bruton’s tyrosine kinase (BTK) Repurposing potential of Acalabrutinib in severe COVID-19 failure Failed Phase II/III

Successful backtesting in a wide range of diseases

Our other predictions are doing just as well. In 16 instances we identified the correct drug target which ended up passing clinical trials.

Further backtesting highlights the validity of causal modeling across disease areas.

Disease Drug Target Gene Mechanism biotx.ai Prediction Clinical Outcome
Cardiovascular and thrombotic disease Coagulation factor X Repurposing potential of factor Xa inhibitors in other cardiovascular disease subtypes success success, Phase III clinical trial
Cardioembolic stroke Coagulation factor XI Protective effects of factor XI inhibition in cardioembolic stroke success success, Phase III trial evidence in venous thromboembolism
Non-alcoholic steatohepatitis Fibroblast growth factor 21 (FGF21) Favourable effects of circulating FGF21 on cardiometabolic biomarkers success success, Phase II clinical trial
Heart failure Glucagon-like peptide 1 (GLP1) Protective effects of GLP1 agonism in heart failure success success, Phase III trial underway (NCT01800968)
Cardiometabolic disease Glucose-dependent insulinotropic polypeptide (GIP) Favourable effects of GIP agonism on bodyweight, lipid traits, coronary artery disease and inflammation success success, phase III clinical trials
Cardiovascular disease Interleukin 6 receptor (IL6R) Beneficial effects of IL6R inhibition in severe COVID-19, increased risk of infectious, allergic and autoimmune disease, identify biomarkers to measure efficacy, repurposing potential in atherosclerotic disease success success, Phase II and III clinical trial evidence
Cardiovascular disease Proproteinconvertase subtilisin/kexin type 9 (PCSK9) Protective effects of PCSK9 inhibition success success, phase III clinical evidence
Coronary heart disease Niemann-Pick C1-like 1 (NPC1L1) Protective effects of NPC1L1 inhibition success success, phase III clinical evidence
Cardiovascular disease Cholesteryl ester transfer protein (CETP) Protective effects of CETP inhibition on cardiovascular disease success success, phase III clinical evidence

biotx.ai enables causal modeling at scale

We enable drug discovery and repurposing via large-scale screening

  • Discover all diseases that can be treated by a given drug
  • Discover all drugs that can be used to treat any given disease
  • Explore all disease mechanisms and pathways

Our platform and biobank built over 5 years enables scaling

28.5KDrugs 500DiseaseCausing Traits 12KDiseases 2 MillionHuman Genomes 1 Million ComputationalExperiments/Day on the Cloud

Customer Validation

Successful use cases by our biotech customers

Immungentics, a German biotech focused on Alzheimerʼs disease, were able to not only find causal biomarkers crucial for their study design, but in addition, multiple used multiple causal models from biotx.aiʼs analyses to better understand the exact mode of action of their drug thiethylperazine via the ABCC1 transporter.

Alfa Intes, a family-owned Italian pharma, through biotx.ai discovered new indications for their drug Indomethacin, and is in the process of picking indication for a further deal with biotx.ai.

Eternygen, a German biotech, found via our platform that one of the potential indications for their compound will not work and has shifted focus onto another indication instead; next deal has been signed.

Pipeline

Learn more about our recent advances