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.

Accelerating the Development of Personalized Cancer Immunotherapy by Integrating Molecular Patients’ Profiles with Dynamic Mathematical Models

“…Virtual patient populations can undergo virtual clinical trials, end points of which are those used in research, for example, for analyzing properties of individual patients, which may affect phenomena on the level of patient population (see below), 22 or in drug development projects, 23 e.g., for examining how shelved drugs can be rescued. 24…”

A model qualification method for mechanistic physiological QSP models to support model‐informed drug development

“…If a qualitative uncertainty persists (i.e., more than one hypothesis is consistent with relevant knowledge and data), its effect on the research question can be explicitly evaluated by maintaining multiple possible model structures and analyzing differences in predicted responses to various in silico experiments. This can be achieved through the use of virtual patients (VPs) – alternative versions of the model in which sensitive pathways or parameters are deliberately varied to explore the systemic effects of these differences.…”

“…At this date, there is a record of publications and conference presentations in a wide variety of therapeutic areas, including type 2 diabetes, type 1 diabetes . rheumatoid arthritis, skin diseases and sensitization, liver toxicity, asthma, bone remodeling, cardiovascular disease, and oncology/immuno‐oncology . Published applications of mechanistic physiological models include clarification of biological mechanisms and hypothesis exploration, target identification and evaluation, compound evaluation or optimization, preclinical to clinical translation, experimental design and protocol optimization, patient stratification, and biomarker identification …”

An Analytics Approach to Designing Combination Chemotherapy Regimens for Cancer

“…Following commonly accepted principles for designing combination chemotherapy regimens (Page and Takimoto 2002), researchers seek to combine drugs that are effective as single agents and that show synergistic behavior when combined; drugs that cause the same toxic effects and that have the same patterns of resistance are not combined in suggested regimens. Molecular simulation is a well-developed methodology for identifying synergism in drug combinations (Chou 2006), and virtual clinical trials, which rely on pharmacodynamic and pharmacokinetic models to analyze different drug combinations, can also be used to suggest new treatments (Kleiman et al 2009). Animal studies and in vitro experimentation can be used to further evaluate novel chemotherapy regimens; results of these preclinical studies are often cited as motivations for phase II studies of combination chemotherapy regimens (Chao et al 2006, Iwase et al 2011, Lee et al 2009.…”