title: Use of virtual patient populations for rescuing discontinued drug candidates and for reducing the number of patients in clinical trials.
abstract: The decreasing cost-efficiency of drug development threatens to result in a severe shortage of innovative drugs, which may seriously compromise patient healthcare. This risk underlines the urgency to change the paradigm in clinical research. Here, we examine a novel concept of conducting virtual clinical trials for efficiently screening drug candidates, and for evaluating their prospects of being brought to the market successfully. The virtual clinical trials are carried out by using virtual patients (denoted Optimata Virtual Patients -- OVPs). The OVP, a set of mathematical algorithms that describe the main pathological and physiological dynamic processes affected by the administered drug, has been shown to accurately predict docetaxel efficacy and safety in individual breast cancer patients. We report a test case in which virtual clinical trials have been conducted by using OVP populations for rescuing a discontinued oncology compound, ISIS-5132 (ISIS Pharmaceuticals Inc.). Our in silico study suggested that ISIS-5132 may be efficacious in combination with another drug, sunitinib malate (Sutent, Pfizer Inc.), for the treatment of prostate cancer. The recommended combined treatment is predicted to result in a higher five-year Progression-Free Survival than monotherapy with either drug alone.
authors.affiliation: Optimata Ltd, Ramat-Gan, Israel.
authors.forename: Marina
authors.initials: M
authors.lastname: Kleiman
authors.forename: Yael
authors.initials: Y
authors.lastname: Sagi
authors.forename: Naamah
authors.initials: N
authors.lastname: Bloch
authors.forename: Zvia
authors.initials: Z
authors.lastname: Agur
mesh_terms: D000465:Algorithms; D002986:Clinical Trials as Topic; D003198:Computer Simulation; D015195:Drug Design; D004341:Drug Evaluation / Q000379:methods*; D056736:Drug Recalls; D004358:Drug Therapy; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D008954:Models, Biological; D011237:Predictive Value of Tests; D056737:Safety-Based Drug Withdrawals; D018401:Sample Size; D013678:Technology, Pharmaceutical; D017211:Treatment Failure