Quantitative Medicine Program

Overview

The Problem

A major challenge in drug development clinical trials is the proper handling of variability and uncertainty. Without a proper statistical approach, drug development risks including more patients than necessary, resulting in excessive costs and resource requirements, or worse, risks including too few patients, resulting in failed trials. Currently available solutions to handle variability and uncertainty are either nonexistent, inefficient, or not readily accessible. Efficient, publicly available, targeted, and regulatory-grade quantitative solutions are needed to address these specific unmet needs in drug development for various disease areas. 

The Solution

The work of the Quantitative Medicine (QuantMed) Program at Critical Path Institute (C-Path) focuses on the development of advanced, regulatory grade, data analysis (quantitative) solutions to accelerate development. Successes include the transformation of drug development paradigms in areas including (but not limited to) Parkinson’s, Alzheimer’s, Type 1 diabetes, tuberculosis, and a wide array of rare diseases. QuantMed leverages knowledge from a network of experts in industry, academia, nonprofit, and regulatory sciences combined with integrated data from multiple sources to develop actionable solutions that combine clinical pharmacology, statistics, mechanistic modeling, artificial intelligence, pharmacometrics, and digital health technology (or remote health technology) data. 

The Impact

The solutions developed by QuantMed are often formally reviewed by regulatory agencies and endorsed for specific applications in drug development. Because QuantMed’s philosophy is one of open science, solutions developed are publicly available as open-source platforms. The QuantMed Program is highly collaborative and advances the development of novel treatments for patients with unmet medical needs.

Projects

Target

Quantitative Systems Pharmacology in Tuberculosis (completed):

• Quantifying interactions between pathogen, host’s immune system and drugs.

Drug

Physiologically-based pharmacokinetic (PBPK) platform (completed):

• Lung + granuloma model, virtual South African population and drug + metabolites library
• Optimizing clinical trail design for the first -in-human studies

Drug-Induced torsade de pointes risk-stratification algorithm (completed):

• Optimizing cardiac electrophysiological monitoring in clinical trials.

Dose

Hollow-fiber system platform for tuberculosis (HFS-TB), qualified by EMA, included in FDA’s TB drug development guidance:

• Predicting clinical dose selection:
  • In-vitro experiments.
  • Estimation of PKPD parameters from experimental data.
  • Monte Carlo simulations to predict clinical dose selection.

Population – PK/PD based analyses for standard of care (SOC) drugs, based on real world patients (completed):

• Optimizing treatment doses for SOC drugs.

Patient and Design

Mild-to-moderate Alzheimer’s disease clinical trial simulation tool (First-ever qualified model by EMA, first-ever endorsed model by FDA):

• Optimizing Phase II and III trial design for dementia:
  • Disease progression model.
  • Drug effect model.
  • Placebo effect model.
  • Drop-out model.

Clinical trial simulation tool for the pre-dementia stage of the Alzheimer’s disease continuum (First-ever model to receive a letter of support by EMA, under review by FDA):

• Optimizing Phase II and III trial design for pre-dementia:
  • Disease progression model.
  • Drug effect model.
  • Placebo effect model.
  • Drop-out model.

Clinical trial simulation tool for early-motor Parkinson’s disease (under review by FDA and EMA):

• Optimizing Phase II and III trial design for early-motor Parkinson’s disease:
  • Disease progression model.
  • Drug effect model.
  • Placebo effect model.
  • Drop-out model.

Biomarkers

Total kidney volume in polycystic kidney disease (qualified by FDA and EMA):

• Total kidney volume (TKV) as a prognostic biomarker for trial enrichment in PKD.
  • Biomarker dynamics model.
  • Clinically-relevant endpoints model.

Dopamine transport imaging (DAT) as an enrichment biomarker for early-motor Parkinson’s disease (qualified by EMA):

• Disease progression model.
• Clinical trial simulator.
• Digital biomarker impact assessment tool.

Endpoints

Model-based meta-analysis of Phase III quinolone trials (TB-REFLECT):

• Predicting clinical benefit/harm in Phase III trials.
  • Quinolone versus standard-of-care efficacy and safety parameters.

D-RSC Duchenne disease progression model (under review by FDA):

• Quantifying longitudinal progression and links to clinically relevant milestones.

Team