9/24/2023 0 Comments Roche machines![]() Today, trials are expensive affairs with big logistical challenges (30% of Phase 3 trials are terminated due to enrollment difficulties). Digital Biomarkers in Clinical Testing: As the number of high-potential drug targets grows, the bottleneck for pharmaceutical companies will increasingly become clinical trials.Machine learning startups such as BenevolentAI claim to be upending and outpace traditional R&D models in pharma with target validation success rates that are 4 times as high. These biotechs are taking advantage of i) declining cost of genomics, ii) huge volumes of new patient data, and iii) applications of machine learning techniques to computational biology. Machine Learning in Drug Discovery: Advances in technology and the introduction of new types of gene and cell therapies have given rise to more and better-funded early-stage biotechs. ![]() Roche’s traditional competitive advantages across each stage of the chain-1) Discovery & Development, 2) Clinical Testing, and 3) Go-to-Market - are being threatened by upstarts across technology and health care armed with new sources of data and machine learning techniques. For Roche in particular, a biologics “patent cliff” looms over its portfolio of cancer drugs, which generated $21 billion in sales last year but will face competition from cheaper biosimilar copies by the end of this year. ![]() But the drug-hunting approach of old-with a cycle time of a decade and a price tag of $2.7 billion and growing-is unsustainable for Roche and its fellow pharmaceutical giants. The traditional business model in pharma hinges on blockbuster drug development, large-scale clinical trials and a robust market access and marketing engine.
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