Email: emailus@vantage-research.net linkedin Careers

At Vantage Research, we develop and use mathematical models of physiology to aid decision-making in the process of drug development. Our work is in the newly emerging field of Quantitative Systems Pharmacology (QSP).

According to the NIH White Paper on QSP [1], “Quantitative and systems pharmacology is an emerging discipline focused on identifying and validating drug targets, understanding existing therapeutics and discovering new ones. The goal of QSP is to understand, in a precise, predictive manner, how drugs modulate cellular networks in space and time and how they impact human pathophysiology. It aims to develop formal mathematical and computational models that incorporate data at several temporal and spatial scales; these models will focus on interactions among multiple elements (biomolecules, cells, tissues etc.) as a means to understand and predict therapeutic and toxic effects of drugs.“

The QSP space with Vantage focus area highlighted1


QSP can impact all stages of the clinical development process of drugs from improving the basic understanding of the disease and predicting drug targets to selecting the right drug, fine tuning the drug dosage and determining the correct drug to be given to different categories of patients (i.e. depending on disease severity and complications).


For a good introduction to modeling and simulation in drug discovery,

[1] “Quantitative and Systems Pharmacology in the post-genomic era: New approaches to discovering drugs and understanding therapeutic mechanisms”, An NIH white paper by the QSP workshop group, Sorger, P., et al, 2011

What we do at Vantage 

Several high-value decisions need to be taken when taking a compound from ‘bench’ to ‘bedside’. These could be related to the mechanism of action (e.g. is the target pathway inhibited sufficiently by the compound?) clinical management (e.g. What is the ideal trial design that optimizes dosing across multiple patient populations?) and interpretation of data from experiments or clinical trials (e.g. Does the slower than expected decay of the compound indicate additional physiological effects?).

We have worked on modeling and simulation projects in several therapeutic areas as well with research teams in early discovery all the way to design of clinical trials. We find that at the most basic level, modeling increases understanding of the connection between basic physiology and clinical outcomes. Large-scale models serve a knowledge-management function – a team’s knowledge (& hypotheses!) of physiology, mechanisms of action, clinical behavior etc. are all aggregated in one tool.”What if?” questions can be simulated readily to evaluate competing hypotheses and discrepancies in the data. We use simulations to evaluate between alternate experimental designs that can cost millions of dollars and recommend optimal course-of-action.

We use Quantitative Systems Pharmacology i.e. mechanistic models of physiology to gain insight into these questions. We extend these models to also study the effect of drugs on Virtual Patient populations and suggest optimum trial designs. We use simulations to evaluate between alternate experimental designs that can cost millions of dollars and recommend optimal course-of-action. Finally, we analyze the results of clinical trials and interpret the results in the light of the information gained from our models. This can be used to refine and improve models for further development.

Our current and past projects have been in several therapeutic areas such as sepsis, diabetes, dermatology, anemia, rheumatoid arthritis, hypertension. These projects can be associated with early discovery teams or clinical trial design in phase 3 or 4. We also develop tools for use in parameter estimation and optimization. This is an area which is very relevant to modeling of physiological systems since several parameters in quantitative models are not directly available and are estimated using indirect evidence.

Tools We Use 

At Vantage, we are familiar with multiple tools to develop and analyse models and have used them in projects.

PhysioLab, JDesigner are tools to create Ordinary Differential Equation models using graphic interfaces that are accessible to both biologists and engineers. These tools facilitate development of models ‘from scratch’, modify existing models for simplification or expansion and to leverage existing assets in these platforms.

R is an open source statistical package used for statistics and graphical purposes. At Vantage, we use R for modeling, parameter estimation using Bayesian inference and for creating graphics of clinical data. R is available across all platforms and computationally intensive simulations can be performed by linking it to C, C++ and python codes.

MATLAB is proprietary computing software used in multiple applications. We have used Matlab to develop models as well as analysis, parameter estimation etc.

Sample Publications 

pdficonQuantitative Systems Pharmacology (QSP) tools to aid in model development and communication: Vantage QSP Modelling Tools (VQMTools)

pdficonDevelopment of Rheumatoid Arthritis QSP model capturing mechanistic pathways and clinical read-outs to enable simulation of novel therapies and trials

pdficonQSP Model of Rheumatoid Arthritis: Capturing range of clinical responses to Methotrexate and anti-TNFα therapies

pdficonSystems approach to immuno-oncology (IO) drug development: Integrating Data and Knowledge

pdficonNetwork Meta-Analysis to evaluate comparative efficacy, progression free survival and tolerability among Immune Checkpoint Inhibitor Therapies (CIT) in Melanoma patients

pdficonModelling in Immuno-Oncology (IO) drug development: Perspective and challenges





pdficonQuantitative Physiologic Model of the Interaction Between Nutrition and Infection to Determine the Energy Available for Growth

pdficonLeveraging a Quantitative Systems Pharmacology Model to Explore the Mechanism of Action of a Novel Basal Insulin Analog

pdficonAn open-source platform for Sensitivity Analysis of QSP models

pdficonDesigning optimal basal insulin analogs using a Quantitative Systems Pharmacology model

pdficonUsing optimization algorithms to estimate parameters in a simple PK model with bifurcations