Chronic Obstructive Pulmonary Disease
Pre-Clinical
Challenge:
Develop a novel compound as a second-line therapy for COPD and investigate mechanistic rationale for possible efficacy/safety advantage vs. competitor
Approach:
Integrate preclinical & clinical information into a COPD PhysioPD Platform, simulate compounds, compare clinical outcomes and biomarkers
Results:
Showed novel compound efficacy to be inferior to competitor under current understanding of mechanism of action (MOA). Demonstrated that the novel compound’s MOA must include additional effect(s)
Impact:
Identified new MOA hypotheses and client initiated experiments to test hypotheses. Avoided possible risks of advancing compound into clinical development with incomplete MOA understanding
Oncology
Phase 1
Challenge:
Identify optimal dose and dosing schedule for a novel oncology antibody therapy with multiple mechanisms
Approach:
Built Platform to identify sensitive pathways in a growth factor-expressing tumor. Simulated treatment with antibody therapy and evaluated impact of dose and schedule variations on sensitive pathways
Results:
Identified key drivers of therapeutic response. Underlying patient characteristics (effector : target cell ratio) had greater impact than variations in drug dose and schedule
Impact:
Increased confidence in clinical trial design and identified potential biomarkers to guide patient inclusion criteria
Cardiovascular Disease
Phase 2
Challenge:
Select next generation compound with optimal binding properties. Translate in vitro results to in vivo animal and human efficacy to inform program decisions for next generation compound advancement
Approach:
Developed CVD Platform of in vitro and in vivo receptor binding and trafficking. Simulated multiple virtual compounds, varying binding properties, to evaluate efficacy
Results:
Identified optimal properties for next generation compounds. Enabled translation of results in vitro to primate and human paradigms
Impact:
Enabled identification and selection of next generation compounds. Informed decision-making using rigorous quantitative estimates of compound efficacy
Type 2 Diabetes
Phase 3
Challenge:
Drug with poorly characterized MOA showed reductions in A1C and plasma glucose. FDA review indicated a perceived inconsistency between A1C and average plasma glucose changes with no obvious explanation
Approach:
Develop a type 2 diabetes PhysioPD™ Platform including Virtual Patients consistent with client Phase 3 trial data. Generate hypotheses to explain observed relationships between A1C and glucose
Results:
Sampling time of fasting plasma glucose likely contributes to the perceived mismatch between A1C and glucose. Variability in dietary carbohydrate between clinical trial sites may impact observed response
Impact:
Informed client strategy for planned FDA discussions. Recommended strategies for future T2D drug trial design
Oncology
Phase 4
Challenge:
Gain a better understanding of response to current treatments. Guide the selection and development of new treatments
Approach:
Developed a PhysioMap®, capturing pathways involved in disease progression and current Standards of Care (SoC). Created a model framework for simulating treatment with SoC and investigational therapies in Virtual Patients
Results:
Gained insight into key disease-related pathways likely to control patient response and non-response
Impact:
Created a systematic, quantitative methodology to explore new treatments. The PhysioPD Platform can be integrated into the patient-specific Systematic Treatment Methodology.
These are just a select few case studies to show the potential of PhysioPD.
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