Systems Pharmacology

Comparison of Virtual Population Generation Methods for Quantitative Systems Pharmacology Models

Andrew Tredennick¹, Matthew Weins¹, Timothy Knab¹, Cole Zmurchok¹, Ahmed Elmokadem²
¹ Metrum Research Group, Boston, MA, USA; ² GlaxoSmithKline (GSK), Collegeville, PA, USA

Presented at the 2026 American Conference on Pharmacometrics (ACoP 2026), an International Conference on Pharmacometrics in the US (ICoP – US), on October 14th, 2026.

Virtual populations (VPops) are widely used in quantitative systems pharmacology (QSP) to represent variability in clinical populations and support model calibration and simulation of treatment outcomes. Practical guidance on selecting among algorithms to generate VPops, and understanding their relative performance, remains limited. The objective of this work was to compare commonly used and emerging VPop generation approaches based on data-simulation matching and computation time.

Using physiologically-based pharmacokinetics-quantitative systems pharmacology model to optimize peptide-drug conjugates design

Introduction:

Peptide-drug conjugates (PDCs) are being developed as new cancer treatments. Compared to larger antibody-drug conjugates (ADCs), they retain the advantage of a targeted therapy, binding to tumor-associated antigens (TAA) to deliver payload, while having better tumor penetration than ADCs. PDCs typically exhibit faster plasma clearance than ADCs, and therefore potentially have less tumor exposure to the drug. The pharmacokinetics is influenced by the molecular weight (MW) of the peptide used in the PDC. A physiologically-based pharmacokinetics (PBPK)-quantitative systems pharmacology (QSP) model would be useful to understand the interplay of pharmacokinetics (PK), tissue disposition, and tumor drug exposure of PDCs.

Methods:

A platform PBPK-QSP model was developed based on a published PBPK model (Li et al., 2019) and a genetic tumor QSP model (Scheuher et al., 2024) by coupling these two models with a MW-based tumor penetration relationship. Through calibrated model simulations and sensitivity analysis, we interrogate how parameters of the peptide (e.g., molecular weight, binding affinity towards TAA) impact the PK, tissue disposition, and tumor drug exposure.

Results:

The model predicted higher tumoral PDC concentration when the peptide was small, corresponding to the knowledge that smaller peptides resulted in better tumor penetration. However, a bell-shaped relationship was predicted between peptide size and PDC or payload exposure. The model predicts that a peptide with MW of approximately 86 kDa results in maximum tumor PDC or payload exposure despite exhibiting more rapid PK clearance than a 150 kDa ADC. This is in contrast with tissue disposition, as larger peptides led to higher non-tumoral tissue exposure. Further sensitivity analysis indicated this relationship is insensitive to tumor characteristics, such as TAA expression, or PDC binding affinity towards the TAA, but is sensitive to the peptide size.

Conclusions:

This work demonstrated that this platform PBPK-QSP model can be a useful tool to guide PDC design and lead selection by optimizing peptide size for PDCs.

Citation Format:

Yuezhe Li, Cole Zmurchok, Daniel C. Kirouac, . Using physiologically-based pharmacokinetics-quantitative systems pharmacology model to optimize peptide-drug conjugates design [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6846.

Crowdsourcing Proposal Supporting Patient Engagement in Parkinson’s Disease: A Digital Research Environment (DRE)-Enabled, Patient Swarm Approach to Develop QSP Models

Parkinson’s disease remains a complex and poorly understood condition, with critical gaps in our knowledge of disease progression, heterogeneity, and mechanisms of action for existing therapies. To address these challenges, we are launching a collaborative, patient-led effort to develop a QSP model for PD—incorporating real-world patient experiences, clinical insights, and crowdsourced literature review within a secure digital research environment. This initiative aims to accelerate the development of more precise, mechanism-informed therapeutic strategies for the PD community.

QSP Modeling of Loncastuximab Tesirine-lpyl Combined with T Cell-Dependent Bispecific Antibodies Bridges Knowledge and Dose Regimen Strategy

Presented at ASCPT Annual Meeting 2025. The virtual population-based lymphoma QSP model enabled systematic exploration of alternate drug combinations, dosing schemes, clinical covariates and resultant effect on anti-tumor activity in silico. The results of the LOTIS-7 study (NCT04970901), a platform study evaluating loncastuximab tesirine in combination with glofitamab, will soon be available to assess the accuracy of the model predictions.

Pharmacometric Machine Learning: Integrating Neural Networks for Flexible, Advanced Covariate Analysis

Presented at ASCPT 2025 Annual Meeting. Neural networks can be integrated with traditional pharmacometric models using several free open-source programming languages. Both Julia and R environments are suitable platforms, but there are tradeoffs regarding development speed, built-in capabilities, and documentation. DCM simplifies the covariate modeling process and uncovers complex, non-linear relationships in computationally efficient workflows.

A Novel Agent-based Computational Model for Liver-targeting, AAV-based Gene Therapies Could Predict Response Durability in Hemophilia B Patients Treated with Etranacogene Dezaparvovec

Co-developed with CSL Behring, this research introduces a high-resolution model that simulates hepatocyte turnover,  episomal retention and coagulation factor IX (FIX) expression  in virtual patients treated with Etranacogene Dezaparvovec ( HEMGENIX).  The model captured clinical variability before recent clinical readouts were available, and projects FIX expression durability  over two decades.

Making Drugs from T Cells: Mathematical Model-Informed Design and Deployment of Next Generation T Cell Therapies

Presented at the Boston Society Gene and Cell Therapy Conference 2025. The development landscape for cell therapies is rapidly evolving, driven by scientific advancements and increasing competitive pressures. Modeling and simulation technologies are playing a critical role in navigating this dynamic environment.

Quantitative Systems Pharmacology Modeling of X-linked Hypophosphatemia Disease Pathway Normalization to Predict the Impact of Burosumab Treatment on Serum Biomarkers in Adult and Pediatric Patients

Presented at ACoP 2024. The Bone Health QSP model was extended by incorporating XLH disease mechanisms and burosumab impact on serum phosphate and other biomarkers using clinical data from adult and pediatric patients with XLH. The model reproduced clinically observed changes in pharmacodynamic markers in both adult and pediatric patients with XLH; normalization of serum phosphate with burosumab treatment was successfully replicated, facilitating a better understanding of burosumab dosing in patients with XLH going forward. The model could potentially be used to optimize treatment in the clinical setting.

Quantitative Systems Pharmacology Modeling of Loncastuximab Tesirine Combined with Mosunetuzumab and Glofitamab Helps Guide Dosing for Patients with DLBCL.

Presented at ACoP 2024. This poster described a QSP model that predicted the efficacy of loncastuximab tesirine (an antibody-drug conjugate) and mosunetuzumab/ glofitamab (T cell engagers) combination therapy, following the protocol of ongoing LOTIS-7 clinical trial. The model predicted keeping patients on combination therapy for longer but reduced the loncastuximab tesirine per treatment cycle would yield more tumor volume reduction.

How to Make a Salad? Rethinking Pharmacometric/QSP Model Composition using Open-Source Julia Tools.

Presented at ACoP 2024. The poster presents a framework for pharmacometric and quantitative systems pharmacology (QSP) model composition using open-source Julia tools. This framework allows for seamless integration and reuse of independent model components, facilitating the creation of complex models from simpler ones, and demonstrating applications in drug interactions, viral dynamics, and bispecific antibody modeling.

Quantitative Systems Pharmacology Modeling of Loncastuximab Tesirine Combined With Mosunetuzumab & Glofitamab Guides Dosing in B-cell Lymphoma

Presented at ACCP Annual Meeting 2024, this poster highlights novel research on innovative combination therapies for relapsed/refractory diffuse large B-cell lymphoma (R/R DLBCL). Our study focuses on a physiologically-based pharmacokinetic (PBPK) and quantitative systems pharmacology (QSP) model for Loncastuximab Tesirine in combination with Mosunetuzumab or Glofitama.

Quantitative Systems Pharmacology Model Predicts Combination Activity of CD19-targeted Loncastuximab Tesirine With Epcoritamab in B-cell Lymphoma

Presented at ACCP Annual Meeting 2024, this poster highlights a combination QSP model for Loncastuximab Tesirine and Epcoritamab in relapsed/refractory diffuse large B-cell lymphoma (R/R DLBCL), predicting potential anti-tumor synergies in proposed clinical trials.

A Quantitative Modeling and Simulation Framework to Support Candidate and Dose Selection of Anti-SARS-CoV-2 Monoclonal Antibodies to Advance Bamlanivimab Into a First-in-Human Clinical Trial

Neutralizing monoclonal antibodies (mAb) have emerged as promising therapeutics for COVID-19, with research efforts accelerated through open-access in silico models. Innovative approaches, including a physiologically-based pharmacokinetic (PBPK) model and a longitudinal SARS-CoV-2 viral dynamic model, facilitated the selection of optimal mAb candidate and therapeutic dose. By utilizing these models, the first-in-human trial of bamlanivimab (NCT04411628) initiated with a conservative dose of 700 mg, aiming to achieve maximum therapeutic effect while ensuring safety and tolerability.

Quantitative systems pharmacology modeling of loncastuximab tesirine combined with mosunetuzumab and glofitamab helps guide dosing for patients with DLBCL

Presented at AACR Annual Meeting 2024. QSP model simulations were used to predict anti-tumor efficacy and guide dosing of the antibody-drug conjugate Loncastuximab tesirine combined with T cell-dependent bispecific antibodies, Mosunetuzumab or Glofitamab for the treatment of B-cell malignancies.

Making drugs from T cells: The quantitative pharmacology of engineered T cell therapeutics

Engineered T cells, particularly CAR T cells, have revolutionized hematological cancer treatment. However, their complexity poses challenges. This research explores how mathematical models, utilizing clinical data, can elucidate relationships between product characteristics, patient physiology, and clinical outcomes, thereby facilitating the development of next-generation cell therapies.

An Open Source Package Suite in Julia to Facilitate QSP Modeling and Simulation

Presented at ACoP14. Here, we introduce a pair of packages PMParameterized.jl which simplifies specification of parameters and initial conditions for Ordinary Differential Equation models, and PMSimulator.jl which enables complex dosing, inputs and events. Together these packages result in stateful, reproducible models and simulation for a quantitative systems pharmacology and pharmacometrics workflow in the Julia language.

Hierarchical Deep Compartment Modeling: A Workflow to Leverage Machine Learning for Hierarchical Pharmacometric Modeling

Oral presentation for ACoP14 Abstract Quality Award winner. The Hierarchical Deep Compartment Modeling (HDCM) framework introduced in this abstract was developed using open-source tools in the Julia Programming Language and integrated Bayesian inference to quantify the uncertainty around the model parameters. It provides a convenient, readily accessible HDCM framework to the pharmacometrics community interested in applying deep learning to hierarchical compartmental models.

In relapsed or refractory diffuse large B-cell lymphoma, CD19 expression by immunohistochemistry alone is not a predictor of response to loncastuximab tesirine

This study explores Lonca’s efficacy across varying CD19 expression levels, revealing its effectiveness in patients, regardless of low or undetectable CD19 levels by conventional methods. The study integrates quantitative systems pharmacology (QSP) modeling to predict treatment responses, indicating that CD19 expression alone may not predict Lonca’s effectiveness, however, response predictions are improved by considering CD19 surface density.

Deep QSP Modeling: Leveraging Machine Learning for QSP Model Development and Evaluation

Presented at ACoP14. DQSP framework successfully implemented a UDE that characterized the PK of remoxipride and its effect on PRL release from lactotrophs to plasma. The model also characterized the positive feedback effect of plasma PRL on lactotroph PRL stimulation using an ANN.

A multi-organ integrated QSP model for hematopoietic stem cell differentiation to predict the immune cell reconstitution in ex-vivo gene therapy

Presented at ACoP14. The differentiation of mammalian hematopoietic stem cells (HSCs) is complex and multiscale, providing an opportunity for mathematical modeling and simulation to aid in mechanistic understanding, and ultimately, to inform drug development efforts. Historically, HSC mathematical models were focused on the development of a subset of cells, but mathematical models encompassing the overall cellular system’s complexity are rarely available. Here, an integrated quantitative systems pharmacology (QSP) model that characterizes multi-organ HSC differentiation was developed by integrating literature models and adding novel features.

Exploring the Influence of Bispecific Antibody Mechanisms on In Vitro Dose Response: Insights from an Open-Science Quantitative Systems Pharmacology Model in Julia

Presented at ACoP14. Analysis of the QSP model found that the internalization rate of the target receptors may be an important, yet often underestimated, factor for understanding bsAb efficacy in vitro. Simulations of the model indicated that high rates of receptor turnover can increase model predictions of efficacy, particularly at higher antibody concentrations.

Hands-On Tutorial: Introduction to Immuno-Oncology (IO) Quantitative Systems Pharmacology (QSP) Modeling Using the Open Source Julia Programming Language

ACoP14 pre-meeting workshop. Highlights include exploring Julia as an open source alternative for QSP support of model-informed drug development, gaining insights into IO QSP modeling for antibody drug conjugate (ADCs) and bispecific mAb (bsAb) therapies to drive your own implementations of Project Optimus, and accessing open source Julia versions of two pivotal IO models and parameterizations.

Quantitative Systems Pharmacology in Model-Informed Drug Development

When tailored for specific questions, QSP models enhance our grasp of clinical observations, guiding research with informed decisions, especially when they incorporate mechanistic insights. These models facilitate the integration of theory and data to test our understanding and explore the root causes of events. By offering quantifiable insights into the interactions within physiology, disease, and pharmacology, QSP models steer further research, impacting study design and biomarker selection. The presentation will encompass case studies on monoclonal antibody (mAb) development in bone health (osteoporosis), infectious disease (SARS-CoV-2), and oncology (relapsed/refractory diffuse large B-cell lymphoma).

A Clinical Quantitative Systems Pharmacology Framework Describing Loncastuximab Tesirine Distribution and to Explore Patient Outcomes From the LOTIS-2 Clinical Trial in Patients With B-cell Lymphomas

A novel QSP framework integrating PBPK modeling with tumor dynamics was developed using literature and in-house data. By employing a virtual population reflecting patients treated with Lonca, it is possible to evaluate indication, clinical population selection, influence of clinical study covariates, disease phenotypes, and CD19 expression levels on clinical responses

Towards the development of a platform PBPK-QSP model in the Julia programming language for evaluating potential toxicities caused by antibody-drug-conjugate therapies

Antibody-drug conjugates (ADCs) are promising anti-cancer drugs but often cause toxicities in clinical use. This poster presents a new model that predicts toxicities of ADCs, including liver and lung effects for drugs like T-DM1 and T-Dxd, and high hepatotoxicity in CM.

Open-Science Immuno-Oncology QSP Modeling Using Open-Source Julia Solvers

As Julia usage continues to grow within regulated biomedical environments, it is vital to ensure analyses are traceable and reproducible. Conducting analyses in an open-science manner is also critical to expand the adoption of Julia and to facilitate the infrastructure growth of Julia as an accessible ecosystem. A step-by-step model-building example of a classic monoclonal antibody-drug conjugate PBPK/tumor dynamics system illustrates how to develop such a reproducible open-science framework.

A physiologically-based pharmacokinetic modeling approach to support candidate and first in human dose selection for bamlanivimab

Tim Knab, Ahmed Elmokadem, Emmanuel Chigutsa, Eric Jordie, Matthew Riggs, Patricia Brown-Augsburger, Christopher Wiethoff, Ajay Nirula, Jenny Y Chien, Lisa O’Brien.  Poster presented at Population Approach Group Europe Annual Meeting; 2-3 and 6-7 September 2021.  Poster I-64.

Dosing regimen optimisation for oseltamivir in immunocompromised paediatric patients with influenza: Extrapolation of efficacy

Jordie EB, Gibiansky L, Knab T, Lemenuel-Diot A, Ravva P, Zwanziger E, Jolivet S, Bhardwaj R, Hernández-Sánchez J, Nasmyth-Miller C, Sturm S. Br J Clin Pharmacol. 2021;1-13. doi:10.1111/bcp.15059

Brexpiprazole Pharmacokinetics in CYP2D6 Poor Metabolizers: Using Physiologically Based Pharmacokinetic Modeling to Optimize Time to Effective Concentrations

Elmokadem A, Bruno CD, Housand C, Jordie EB, Chow CR, Lesko LJ, Greenblatt DJ.  J Exp Pharmacol.  2021; 1– 10. doi:10.1002/jcph.1946

CPT: Pharmacometrics & Systems Pharmacology – Inception, Maturation, and Future Vision.

Rowland Yeo K, Hennig S, Krishnaswami S, Strydom N, Ayyar VS, French J, Sinha V, Sobie E, Zhao P, Friberg LE, Mentré F.
CPT Pharmacometrics Syst Pharmacol. 2021 Jul;10(7):649-657. doi: 10.1002/psp4.12680.

Development and evaluation of a predictive model of hyperphosphatemia induced by inhibition of FGFR by extending an existing multiscale systems pharmacology

Matthew M. Riggs, Howard A. Ball, Kanji Komatsu, Akihiro Yamada.  Poster presented at 2020 American Conference on Pharmacometrics (ACoP11) virtual meeting. 10 November 2020.

Model code and supporting material is available here.

A Semi-physiological Population Pharmacokinetic Model Developed Using Clinical Dose Escalation and Dose Confirmation Data for an Oral Fixed-Dose Combination of CDA Inhibitor Cedazuridine with Decitabine (ASTX727) in Subjects with Myelodysplastic Syndromes

Eric Burroughs Jordie, Ahmed Elmokadem, Mohammad Azab, and Aram Oganesian

Presented by Eric Jordie at American Conference on Pharmacometrics 2019.

Bridging Knowledge Gaps in PBPK through Open Science

Presented by Marc Gastonguay, Ph.D at Development of Best Practices in Physiologically Based Pharmacokinetic Modeling to Support Clinical Pharmacology Regulatory Decision -Making Sponsored by the Office of Clinical Pharmacology OTS, CDER, U.S. Food and Drug Administration (FDA) on 18 November 2019. Event information here. Slides are here

Transparent, Open and Reproducible PBPK and QSP Modeling and Simulation Using an R-Based Framework

Presented by Ahmed Elmokadem, Ph.D at the 9th Annual CTSI Disease and Therapeutic Response Modeling and Simulation Symposium at Indiana University School of Medicine on November 12, 2019. Event details can be found here.

Open-Source and Open-Science to Progress the Integration of Pharmacometrics and Systems Pharmacology

Riggs, Matthew. Presentation at ISoPNE iPSP event. Cambridge, MA. 26 Aug 2019.

Development of an open-source physiologically-based pharmacokinetic model to predict maternal-fetal exposures of CYP450-metabolized drugs

Gastonguay MS, Russell S, Freling R, Riggs M, Kay K, Utsey K, Elmokadem A.

Poster presented at 2019 American Society for Clinical Pharmacology & Therapeutics (ASCPT). Washington DC. 14 March 2019.

Poster presented at ISoP Regional QSP Day 2019. Princeton, NJ. 16 July 2019.

Systems Pharmacology: A day in the life

Riggs MM. Slides presented at AAPS Forum to Connect Predictive Modelers in Boston, MA. 6 May 2019.

When learn/confirm leads to expand/understand: The expanding role of quantitative systems pharmacology in the betterment of therapeutics development

Riggs MM. Clin Pharmacol Ther. 98:394. 18 December 2018. doi: 10.1002/cpt.1287

A perspective on the state of pharmacometrics and systems pharmacology integration

Trame MN, Riggs MM, Biliouris K, Marathe D, Mettetal J, Post TM, Rizk ML, Visser SAG, Musante CJ.  CPT Pharmacometrics Syst Pharmacol. doi:10.1002/psp4.12313. 21 August 2018.

Systems pharmacology model development to provide physiologically based interpretation and drug development decision support in osteoporosis and other bone mineral-related diseases

Riggs, M.  Presented at Pharmacology 2016, annual meeting of the British Pharmacological Society, London, December 2016.

Comparing the performance of four open-source methods for multiple parameter estimation in a systems pharmacology model

Eudy RJ, Riggs MM, Baron K.  Presented at the Sanofi–MSISB Mount Sinai Systems Pharmacology Symposium, June 2016.

Simulation from ODE-based population PK/PD and systems pharmacology models in R with mrgsolve

Baron KT and Gastonguay MR.  Presented at the 6th American Conference on Pharmacometrics (ACoP), Arlington, VA; October 2015.

Linking a mechanistic model of bone mineral density to a time-to-event model of fracture

Eudy RF, Gillespie WR, Riggs MM, Gastonguay MR.  Presented at the 6th American Conference on Pharmacometrics (ACoP), Arlington, VA; October 2015.

Modeling & simulation: Filling the knowledge gap in rare diseases. Return on Investment on the Utilization of Systems Pharmacology and pharmacometrics in drug development for rare diseases: challenges and opportunities

Gastonguay MR and Godfrey CJ.  American College of Clinical Pharmacology (ACCP) Workshop, September 26, 2015.

Connecting the dots: Linking osteocyte activity and therapeutic modulation of sclerostin by extending a multiscale systems model

Eudy RJ, Gastonguay MR, Baron KT, and Riggs MM.  CPT: Pharmacometrics & Systems Pharmacology. 2015;4(9):527-536. doi:10.1002/psp4.12013.

Sclerostin-mediated osteocyte control in bone remodeling: Extension of a multiscale systems model to consider new therapies for osteoporosis

Eudy RJ, Gastonguay MR, Baron KT, Riggs MM.  Presented at the Annual Meeting of the Population Group in Europe (PAGE), Hersonnisos, Greece; June 2015.

FDA advisory meeting clinical pharmacology review utilizes a quantitative systems pharmacology (QSP) model: A watershed moment?

Peterson MC and Riggs MM.  CPT: Pharmacometrics & Systems Pharmacology, 2015 Mar; 4(3).

Extensions of a multiscale systems pharmacology model of bone mineral balance and its regulation of bone health

Riggs MM. Presented at Sunrise Session: Integration of Systems Modeling in Clinical Development: Successes, Challenges, and Future Outlook at the American American Society for Clinical Pharmacology and Therapeutics Annual Meeting, 2013.

Systems pharmacology model development to provide physiologically-based interpretation and drug development decision support in osteoporosis and other bone-related diseases

Riggs M.  Presented at the Annual Meeting of the American Society for Clinical Pharmacology and Therapeutics, 2013.

An evaluation of calcilytic effects on parathyroid hormone and bone mineral density response using a physiologically-based, multiscale systems pharmacology model

Kyle T. Baron, Matthew M. Riggs, Ryoko Sawamura, Takako Shimizu, Fumihiko Okada, Jin Zhou, Takahiro Shibayama, Mendel Jansen. Presented at the American Society of Bone and Mineral Research (ASBMR), October, 2013.

Predicting nonlinear changes in bone mineral density over time using a multiscale systems pharmacology model

Peterson MC and Riggs MM. CPT Pharmacometrics Syst Pharmacol. 2012 Nov; 1(11): e14. Published online 2012 Nov 14. doi: 10.1038/psp.2012.15

Integrated pharmacometrics and systems pharmacology model-based analyses to guide GnRH receptor modulator development for management of endometriosis

Riggs MM, Bennetts M, van der Graaf PH, and Martin SW.  CPT Pharmacometrics Syst Pharmacol. 2012 Oct; 1(10): e11. Published online 2012 Oct 17. doi: 10.1038/psp.2012.10

Qualification of a physiologically-based model for predicted bone marker and bone mineral density changes associated with denosumab treatment

Riggs MM, Baron KT, Plan EL, Gastonguay MR. Presented at American Society of Bone Mineral Research (ASBMR) Annual Meeting, Minneapolis, MN; October 14, 2012 (Abstract SU0363).

Bayesian joint modeling of bone mineral density and repeated time-to-fracture event for multiscale bone systems model extension

Plan EL, Baron KT, Gastonguay MR, French JL, Gillespie WR, and Riggs MM. Presented at the 21st Annual Meeting of the Population Approach Group Europe (PAGE), Venice, Italy, 2012, Abstract 2592.

Systematic extension of a physiologic model of bone and calcium homeostasis

Riggs MM. . Presented at the AAPS Biotechnology Conference, San Diego, CA; May 22, 2012.

Multiscale physiology-based modeling of mineral bone disorder in patients with impaired kidney function

Riggs MM, Peterson MC, Gastonguay MR. The Journal of Clinical Pharmacology. 52(1 Suppl):45S-53S. January 2012. doi:10.1177/0091270011412967

 

Applying a multiscale physiologic system model to evaluate bone-related disease and therapeutic responses

Riggs MM. Presented at First Indiana Clinical and Translational Sciences Institute (CTSI) Symposium on Disease and Therapeutic Response Modeling. Indiana University – Purdue University Indianapolis; Indianapolis, IN; 13 November 2011.