Supercharging QSP with a Comprehensive View of the Drug Development Process

AI scales QSP modeling to deliver superior understanding of anticipated clinical outcomes

In the ever-evolving landscape of drug development, scientists are continually seeking innovative approaches to streamline processes, reduce costs, and enhance efficiency. One such approach is Quantitative Systems Pharmacology (QSP) modeling, a multidisciplinary field that integrates mathematics, biology, and pharmacology to facilitate a deeper understanding of the complexities underlying disease and drug response. With the growing prominence of Artificial Intelligence (AI), QSP modeling is poised for transformative advancements that could revolutionize the drug development pipeline.

Understanding Quantitative Systems Pharmacology Modeling

Quantitative Systems Pharmacology (QSP) modeling is a computational framework used to simulate the physiological and pathological processes involved in drug action. Unlike traditional pharmacokinetic/pharmacodynamic (PK/PD) models, which focus on describing drug concentrations and their effects, QSP models delve deeper into the intricate interactions between drugs, biological systems, and diseases. By incorporating molecular pathways, cellular dynamics, and organ-level physiology, QSP models provide a more holistic view of disease pathology, a drug’s mechanism of action and its pathway-based behavior within the human body. QSP models capture the dynamic interplay between drug compounds and biological entities such as proteins, genes, and signaling pathways. In this manner, QSP models enable the exploration of therapeutic interventions.

Limitations of Traditional QSP Modeling

While QSP offers numerous advantages, it also comes with several limitations that researchers must navigate. Here are four key limitations of QSP modeling:

  1.  Inadequate data and poor data quality - The success of QSP models heavily relies on the availability and quality of experimental data. While technological advancements have increased data generation, obtaining comprehensive and reliable datasets across all relevant biological phenomena important for drug development remains a challenge. Incomplete or inconsistent data can lead to gaps in model accuracy and reliability, particularly in disease states or physiological conditions where data may be scarce or less well-characterized.
  1. Difficult integration of data from diverse sources - In pursuit of understanding the interdependencies between a drug’s chemistry, disease pathology and biological systems, QSP models must integrate data from diverse sources—such as omics data, clinical data, and literature findings. This integration requires sophisticated methodologies to harmonize and reconcile discrepancies. This process is crucial for constructing robust QSP models that accurately reflect biological realities and can effectively predict drug responses or disease outcomes. Researchers must carefully validate model parameters against experimental data to ensure the model's reliability and predictive capability.
  1. Complexity and parameterization challenges - QSP models aim to capture the intricate interactions within biological systems, requiring detailed knowledge of pathways, kinetics, and pharmacodynamics. One significant challenge is the complexity of parameterization—accurately defining and quantifying the multitude of parameters that govern these models. Parameters such as binding affinities, turnover rates of biomolecules, and tissue-specific distributions are often challenging to measure experimentally and can vary widely across different individuals or disease states. This variability can introduce uncertainties and affect the predictive power of the model. This complexity also results in limiting QSP models to specific disease areas. Indication specific QSP models cannot be readily modified and adapted to different disease areas. This specificity also impacts traditional QSP model accuracy, as the diversity of biological systems is typically simplified, as noted above.
  1. Limitations in predictive power - Despite their sophistication, QSP models inherently involve simplifications and assumptions to help represent complex biological systems. These simplifications can lead to limitations in predictive power, particularly when extrapolating beyond the conditions or contexts for which the model was originally developed. Variability in biological responses among individuals or populations, as well as the dynamic nature of diseases and drug responses, pose additional challenges. QSP models may struggle to capture the full spectrum of variability and adaptability observed in clinical settings. Thus, while QSP models can provide valuable insights into drug mechanisms and disease processes, their predictive capabilities may be constrained in certain scenarios.
  1. Limited model personalization and virtual trials are possible - QSP models rely heavily on statistical methods to capture inter-subject variability. Given the amount of missing data, such statistical methods are frequently unreliable. Given this unreliability, the use of QSP models to determine outcomes in virtual clinical trials is fraught with danger. Further, the difficulty in integrating multimodal data (e.g., clinical and genetic) in QSP models, it is difficult to stratify patients based on their likelihood of experiencing specific outcomes.

How BIOiSIM Supercharges Quantitative Systems Pharmacology Modeling with Hybrid AI

AI technologies offer unprecedented opportunities to enhance QSP modeling capabilities, accelerating the drug development process and improving decision-making. Let’s discuss four specific ways VeriSIM Life’s BIOiSIM platform augments deficiencies in traditional QSP modeling by integrating AI and knowledge-based models.

  1. Robust synthetic data generation to address data availability and quality issues - The use of AI-enabled technologies in BIOiSIM provide a robust process to generate synthetic data to fill in data gaps and enable accurate predictions even in data-sparse scenarios. These data gaps include critical biological parameters such as target expression level, target engagement, drug-protein binding. BIOiSIM utilizes advanced AI methods for data imputation to fill in these gaps.
  1. Reducing challenges with data integration from diverse sources - AI algorithms can efficiently integrate diverse datasets, including omics data, clinical trial results, and real-world evidence. By extracting meaningful insights from large-scale data repositories, BIOiSIM’s AI-powered QSP models can uncover hidden relationships and identify novel drug targets. These algorithms excel at handling large and heterogeneous datasets, making them invaluable for integrating and mining diverse sources of biomedical data. AI techniques such as deep learning can analyze genomics, transcriptomics, proteomics, and metabolomics data to elucidate molecular pathways underlying disease and drug response. Furthermore, AI can help in clinical trial data analysis because AI-powered algorithms can extract insights from clinical trial data, including patient demographics, treatment regimens, and outcomes. By identifying predictive biomarkers and treatment responders, AI enhances the design and interpretation of clinical trials. Thirdly, AI enables real-world evidence utilization in that AI algorithms enable the analysis of real-world data from electronic health records, wearables, and patient-reported outcomes. By leveraging real-world evidence, QSP models can validate hypotheses, identify unmet medical needs, and inform treatment decisions.
  1. Reducing model parameterization and calibration burden - Parameterizing and calibrating complex QSP models traditionally require extensive manual effort and expertise. AI techniques and optimization algorithms can automate these processes, reducing model development time and improving accuracy. Parameterizing and calibrating QSP models require iterative optimization of model parameters to fit experimental data. AI techniques offer several advantages in this regard, the first being automated parameter estimation; machine learning algorithms, such as Bayesian inference and neural networks, can automate the process of parameter estimation. By learning from experimental data, AI-driven algorithms optimize model parameters to accurately reproduce observed biological phenomena. Secondly, AI algorithms can quantify uncertainty associated with model parameters and predictions. Bayesian optimization and Monte Carlo methods enable the generation of probabilistic models, enhancing confidence in model predictions and decision-making through the incorporation of parameter- and observational variability.
  1. High predictive capability - AI algorithms assist in population modeling because they enable the development of population-based QSP models that capture inter-individual variability in drug response. By analyzing large-scale datasets, such as electronic health records and clinical trial data, AI-driven models can predict drug efficacy and safety across diverse patient populations. These population-based and AI-enhanced QSP models provide comprehensive insights into a therapy’s effectiveness over time at a population level—thereby providing statistical outcomes in human trials.
  1. Model personalization and virtual trials - AI-enabled QSP models can be personalized to capture inter-individual variability in drug response. By incorporating patient-specific characteristics such as genetic polymorphisms and physiological parameters, virtual clinical trials can simulate the efficacy and safety of drug candidates in diverse patient populations. AI facilitates the personalization of QSP models to account for individual differences in drug metabolism, physiology, and disease pathology. One way in which AI-driven QSP models can do this is by generating virtual patient cohorts representing diverse demographic and clinical characteristics. By simulating virtual trials across heterogeneous populations, QSP models identify subgroups with differential treatment responses and guide personalized therapeutic interventions. AI-enabled QSP models can also assist in risk prediction and stratification, because AI algorithms enable the identification of patients at increased risk of adverse drug reactions or treatment failure. By integrating clinical and genetic data, QSP models stratify patients based on their likelihood of experiencing specific outcomes, facilitating risk mitigation strategies.

BIOiSIM vs other methods of QSP modeling

At the most fundamental level, BIOiSIM’s hybrid AI-driven approach differs from other QSP methods by the simplicity of its investigational requirement: only the SMILES string/chemical structure of the therapeutic is needed to predict comprehensive outcomes that encompass the therapy’s efficacy for the disease of interest and safety, and its physiological disposition. This simplicity makes BIOiSIM immensely generalizable across diseases. BIOiSIM captures these comprehensive outcomes, that relate to the likelihood of translational success, in a Translational Index score. With the help of state-of-the-art quantum mechanical and molecular modeling methods, the SMILES string is converted to a vector of numbers capturing the drug’s physicochemical properties. This vector of numbers is then fed into AI models to, first, determine parameters required to enable the requisite predictions (e.g., Translational Index). Subsequently, these requisite predictions are enabled by a multitude of AI and knowledge-based models that relate to the therapy’s efficacy, safety, and physiological disposition for the desired cohort.

Other methods of QSP modeling require much more complex and cumbersome sets of inputs to get the required outcomes. As these inputs are not often available, the many simplifying assumptions required negate the generalizability and accuracy of these QSP methods.


Quantitative Systems Pharmacology (QSP) modeling represents a powerful approach to drug development, offering insights into the complex interactions between drugs, biological systems, and diseases. However, there are several limitations in the traditional QSP methods, including lack of quality data and generalizability. By infusing knowledge-based modeling with targeted AI pipelines, BIOiSIM overcomes key QSP modeling challenges and accelerates the translation of scientific discoveries into clinically relevant therapies. In general, as AI continues to further advance, its integration into QSP modeling holds immense promise for improving patient outcomes and revolutionizing the pharmaceutical industry.

Related reading

Quantitative systems pharmacology: Landscape analysis of regulatory submissions to the US Food and Drug Administration

An AI Approach to Generating MIDD Assets Across the Drug Development Continuum

Learn about BIOiSIM’s hybrid artificial intelligence, which integrates advanced QSP modeling