Biotx.ai

Biotx.ai, established in 2017 in Potsdam, Germany, operates at the intersection of biotechnology and artificial intelligence to de-risk and accelerate drug development. The company was co-founded by CEO Marco Schmidt and Joern Klinger. Schmidt's background includes a PhD in Chemistry, a post-doctorate at the University of Cambridge, and experience at the fragment-based drug discovery company Astex. This exposure revealed the limitations in predicting clinical efficacy, which became the foundational problem Biotx.ai aims to solve. Klinger, after studying how diverse human learning can improve AI during his PhD, became fascinated with genomics and joined Schmidt to launch the company.

The firm's core business revolves around providing predictive biomarkers and decision support for precision medicine, catering to biopharma companies and contract research organizations (CROs). Its business model involves forming strategic partnerships, out-licensing drug assets, and providing its platform to clients, which has resulted in significant contracts for partners like Simbec-Orion. Revenue is generated through these partnerships, including milestone payments and future royalties on products developed using their discovered targets. For instance, a partnership with HeartBeat.bio aims to identify novel targets for heart failure, where Biotx.ai is eligible for such payments.

Biotx.ai's main offering is an AI-driven platform that utilizes causal modeling to predict the success of drug candidates. The platform analyzes what the company terms "wide data"—complex patterns from smaller genomic datasets—as opposed to the "big data" used by many machine learning algorithms. It leverages a proprietary dataset mapping the human genome's impact on biomarker levels and disease risk, screening thousands of genes and markers across numerous diseases. A key feature is the use of Mendelian randomization and colocalization to establish causal links between drug targets and diseases. The company also employs "Synthetic Clinical Trials," using its biobank of millions of genomes to test hypotheses and predict efficacy and side effects, thereby reducing reliance on traditional, often unsuccessful, wet lab experiments. This approach was notably used to identify CDK6 as a potential drug target for treating critically ill COVID-19 patients, a finding later confirmed by in-vitro testing.

Keywords: predictive biomarkers, precision medicine, causal modeling, drug discovery, AI in biotech, genomic data analysis, synthetic clinical trials, Mendelian randomization, drug target identification, patient stratification, biopharma services, CRO partnerships, computational genomics, clinical trial prediction, wide data, drug development, disease biology, genetic validation, heart failure research, oncology drug assets