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): FAQ Icon
  • Quantifying interactions between pathogen, host’s immune system and drugs.

Drug

Physiologically-based pharmacokinetic (PBPK) platform (completed): FAQ Icon
  • 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): FAQ Icon
  • 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: FAQ Icon
  • 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): FAQ Icon
  • 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): FAQ Icon
  • 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): FAQ Icon
  • 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): FAQ Icon
  • 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): FAQ Icon
  • 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): FAQ Icon
  • Disease progression model.
  • Clinical trial simulator.
  • Digital biomarker impact assessment tool.

Endpoints

Model-based meta-analysis of Phase III quinolone trials (TB-REFLECT): FAQ Icon
  • 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): FAQ Icon
  • Quantifying longitudinal progression and links to clinically relevant milestones.

Team

Klaus Romero, MD, MS
Chief Executive Officer, Chief Science Officer

Shu Chin Ma, PhD, MSc, M. Phil,
EMBA,
Vice President, Model-informed Drug Development and Quantitative Medicine

Jagdeep Podichetty, PhD
Senior Director of Predictive Analytics

Yi Zhang, PhD
Director of Pharmacometrics

Sakshi Sardar, PhD
Senior Director, Digital and Precision Medicine

Kimberly Collins, PhD
Senior Quantitative Medicine Scientist, Pharmacometrics

Luke Kosinski, PhD
Scientific Director, Regulatory Strategy

Nicholas Henscheid, MS, PhD
Quantitative Medicine Scientist

Zihan Cui, PhD
Senior Quantitative Medicine Developer

Lauren Quinlan
Quantitative Medicine Developer II

Wes Anderson
Quantitative Medicine Scientist

Ruby Abrams, PhD
Quantitative Medicine Scientist, Digital and Precision Medicine

Grace Lee, PhD
Quantitative Medicine Scientist, Digital and Precision Medicine

Rachel Xu, MS
Quantitative Medicine Developer

Francisco Morales, PhD
Quantitative Medicine Scientist

Christine Miller
Associate Director

Grace Erhart
Project Manager II

Bri Sullivan
Project Coordinator II

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