Correction to: A quantitative systems pharmacology (QSP) model for Pneumocystis treatment in mice

AZD5305 is a potent and selective PARP1 inhibitor and trapper which is hypothesized to improve therapeutic index over first generation nonselective PARP inhibitors. AZD5305 demonstrated significant and sustained antitumor activity in multiple BRCA1/2 mutant xenograft models. Here we present a mechanistic Quantitative Systems Pharmacology (QSP) model to analyze dose-dependent antitumor activity of AZD5305 (0.01-10 mg/kg) across a selection of xenograft models with different homologous recombination repair (HRR) status (Capan-1, DLD-1 BRCA2 KO, HBCx-9, HBCx-17, MDA-MB-436 and SUM149PT). A QSP model was developed based on a system of ordinary differential equations (ODEs) to address formation and repair of trapped PARP-DNA fragments and longitudinal changes in tumor size as a function of pharmacokinetic (PK) profiles in individual animals. Tumor growth data as well as intratumoral PARylation inhibition from xenograft models were utilized for model development and qualification. Model parameters characterizing intrinsic tumor growth and cancer cell sensitivity to accumulated DNA damage, were set to be different across xenograft models, to provide unbiased data reproduction. Sensitivity analyses were performed to identify model parameters which have the most impact on differential antitumor activity observed across various xenograft models. Maximal antitumor efficacy was seen at 0.1 to 1 mg/kg AZD5305, depending on the tumor model. Exposures at 1 mg/kg were similar to those causing peak PARP1 trapping in vitro. The QSP model adequately captures antitumor activity across different xenograft models. Simulations indicate antitumor activity of AZD5305 was driven mainly by differences in the HRR status-related model parameter (khrr). Xenograft models with HRR deficiency such as HBCx-17, DLD-1 BRCA2 KO and MDA-MB-436 (with a very low khrr) were the most sensitive to AZD5305 and treatment led to tumor regressions. In contrast, tumor models with partial sensitivity, such as HBCx-9, Capan-1, SUM149PT (with khrr up to 1000-fold higher than in the sensitive tumors), AZD5305 only achieved tumor growth inhibition. Dosing AZD5305 at 0.03 mg/kg daily was associated with tumor regression in HBCx-17 and MDA-MB-436 xenografts, whereas 1 mg/kg daily dosing was required to achieve tumor regression in the DLD-1 BRCA2 KO model, and maximal tumor growth inhibition in less sensitive models. Further biomarker analyses to assess functional HRR status (e.g. via RAD51 foci score) in these xenograft models is ongoing to validate model estimated khrr parameters. The calibrated model was used to predict antitumor activity of AZD5305 at clinically relevant exposures observed in the phase I clinical study PETRA. Model-based simulations indicated near maximal efficacy at clinical doses equivalent to 1 mg/kg AZD5305 exposure in xenograft models. Citation Format: Ganesh Moorthy, Veronika Voronova, Cesar Pichardo, Kirill Peskov, Giuditta Illuzzi, Anna Staniszewska, Mark Albertella, Holly Kimko. A Quantitative Systems Pharmacology (QSP) model to characterize dose-dependent antitumor activity of AZD5305, PARP1 selective inhibitor, across multiple xenograft models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2794.

Physiological Indirect Response Model to Omics-Powered Quantitative Systems Pharmacology Model

A Quantitative Systems Pharmacology Model for the Coagulation Network Describes Biomarker Changes Observed in a Clinical Study with FXa Variant and Predicts Age-Associated Biomarker Variations

Simple SummaryMathematical and computational models, such as quantitative systems pharmacology (QSP) models, are becoming a popular tool in drug discovery. The advancement of imaging techniques creates a unique opportunity to further expand these models and enhance their predictive power by incorporating characteristics of individual patients embedded in the image data obtained from tissue samples. The aim of this study is to develop a platform which combines the strength of QSP models and spatially resolved agent-based models (ABM), and create a model of cancer development in the context of anti-cancer immunity and immune checkpoint inhibition therapy. The model can be applied in virtual clinical trials and biomarker discovery to help refine trial design.Quantitative systems pharmacology (QSP) models have become increasingly common in fundamental mechanistic studies and drug discovery in both academic and industrial environments. With imaging techniques widely adopted and other spatial quantification of tumor such as spatial transcriptomics gaining traction, it is crucial that these data reflecting tumor spatial heterogeneity be utilized to inform the QSP models to enhance their predictive power. We developed a hybrid computational model platform, spQSP-IO, to extend QSP models of immuno-oncology with spatially resolved agent-based models (ABM), combining their powers to track whole patient-scale dynamics and recapitulate the emergent spatial heterogeneity in the tumor. Using a model of non-small-cell lung cancer developed based on this platform, we studied the role of the tumor microenvironment and cancer–immune cell interactions in tumor development and applied anti-PD-1 treatment to virtual patients and studied how the spatial distribution of cells changes during tumor growth in response to the immune checkpoint inhibition treatment. Using parameter sensitivity analysis and biomarker analysis, we are able to identify mechanisms and pretreatment measurements correlated with treatment efficacy. By incorporating spatial data that highlight both heterogeneity in tumors and variability among individual patients, spQSP-IO models can extend the QSP framework and further advance virtual clinical trials.

Systems pharmacology approaches have the capability of quantitatively linking the key biological molecules relevant to a drug candidate's mechanism of action (drug-induced signaling pathways) to the clinical biomarkers associated with the proposed target disease, thereby quantitatively facilitating its development and life cycle management. In this review, the model attributes of published quantitative systems pharmacology (QSP) modeling for lowering cholesterol, treating salt-sensitive hypertension, and treating rare diseases as well as describing bone homeostasis and related pharmacological effects are critically reviewed with respect to model quality, calibration, validation, and performance. We further reviewed the common practices in optimizing QSP modeling and prediction. Notably, leveraging genetics and genomic studies for model calibration and validation is common. Statistical and quantitative assessment of QSP prediction and handling of model uncertainty are, however, mostly lacking as are the quantitative and statistical criteria for assessing QSP predictions and the covariance matrix of coefficients between the parameters in a validated virtual population. To accelerate advances and application of QSP with consistent quality, a list of key questions is proposed to be addressed when assessing the quality of a QSP model in hopes of stimulating the scientific community to set common expectations. The common expectations as to what constitutes the best QSP modeling practices, which the scientific community supports, will advance QSP modeling in the realm of informed drug development. In the long run, good practices will extend the life cycles of QSP models beyond the life cycles of individual drugs.

[Introduction] Quantitative Systems Pharmacology (QSP) modeling is a promising technique for model-informed drug discovery and development, and various QSP models for immuno-oncology (IO) have been published. Syngeneic tumor mice are often used for in vivo pharmacology study, and many kinds of IO QSP models have been reported to understand in vivo data and make prediction. However, published QSP models have varying structures across tumor types that makes it difficult to analyze data across different syngeneic tumor models. In addition, there are few QSP models calibrated by actual data of tumor infiltrating lymphocyte (TIL) dynamics. In this study, we present platform IO QSP modeling for syngeneic tumor mice (MC38, B16F10, CT26, 4T1 and LLC1) with a unified structure based on observed data of TIL dynamics and antitumor efficacy of anti-programmed cell death-1 (anti-PD-1) treatment. [Methods] (Mouse study for TIL dynamics) Five mouse tumors were inoculated into C57BL/6 (MC38, B16F10, LLC1) or BALB/c (CT26, 4T1). Tumors were sampled at three time points (mean tumor volume was about 50 mm3, 300-700 mm3 and 500-2000 mm3) and immune cells in the tumors were analyzed by flow cytometry. (IO QSP platform development) The structure of IO QSP platform was based on a published QSP model for CT26-bearing mice [1] and modified with reference to a comprehensive IO QSP model for breast cancer in human [2] to improve physiological interpretability of model components. The TIL dynamics data and published anti-mouse PD-1 (anti-mPD-1) antibody efficacy data for syngeneic tumor mice [3] were used for model calibration. The platform model was validated by confirming predictability of combination therapy of anti-mPD-1 antibody with a multiple kinase inhibitor (lenvatinib) for syngeneic tumor mice. [Results] The IO QSP platform model contains 12 tumor-specific parameters for each tumor type of syngeneic mice and successfully captured the observed TIL dynamics and antitumor effect of anti-mPD-1 antibody treatment. Mechanism of action of lenvatinib was incorporated into the IO QSP platform and calibrated with published data. The final model was successfully validated by comparing simulation and observation of combination therapy of anti-mPD-1 antibody with lenvatinib. [Conclusions] The IO QSP platform was established for several types of syngeneic tumor mice, which captured TIL dynamics and antitumor efficacy of anti-mPD-1 antibody. This platform model enables us to test a hypothesis by incorporating candidate compounds, to support study design with a translational biomarker and to investigate combination strategies, thus having the potential to facilitate new drug development. [References] [1] Kosinsky Y, et al. J Immunother Cancer. 2018;6(1):17. [2] Wang H, et al. Front Bioeng Biotechnol. 2020;8:141. [3] Georgiev P, et al. Mol Cancer Ther. 2022;21(3):427-439. Citation Format: Takeshi Nakayama, Aya Kikuchi, Kota Toshimoto, Hiroyuki Sayama, Taisuke Nakazawa, Masayo Oishi. Establishment of a quantitative systems pharmacology platform for syngeneic tumor mouse models: Application in immuno-oncology drug development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6274.

Quantitative systems pharmacology (QSP) models have emerged as useful tools for evaluating the efficacy of Alzheimer's disease (AD) therapies. Bringing together a clinical focus with the mechanistic detail of systems biology, QSP models are well suited to the complexity of AD and have been used to predict treatment outcomes and support regulatory submissions. Therapies targeting the amyloid pathway are prominent in the AD clinical trial landscape, with anti-amyloid monoclonal antibodies representing the first approved disease-modifying therapies. To inform and facilitate future QSP model development, a systematic review of published QSP models focused on amyloid-targeting therapies for AD was completed. The PubMed and Web of Science databases were searched on February 1, 2025, identifying 540 candidate publications. Predefined exclusion and inclusion criteria were applied to identify seven published AD QSP models used to simulate treatment effects for one or more anti-amyloid therapies. The structure, development, and predictions of the models were summarized. Shared and contrasting model features were identified across included models. A set of model quality features was scored against a checklist of 15 criteria adapted from "best practice" guidelines for QSP. Model quality scores were generally low, ranging from 40% to 53%. Key quality issues related to model validation and reproducibility were identified; in particular, none of the seven papers provided executable model code. This systematic review provides useful context to support ongoing efforts to develop and refine QSP models such that they may better inform therapeutic strategies for the treatment of AD.

Despite the numerous therapeutic options to treat bleeding or thrombosis, a comprehensive quantitative mechanistic understanding of the effects of these and potential novel therapies is lacking. Recently, the quality of quantitative systems pharmacology (QSP) models of the coagulation cascade has improved, simulating the interactions between proteases, cofactors, regulators, fibrin, and therapeutic responses under different clinical scenarios. We aim to review the literature on QSP models to assess the unique capabilities and reusability of these models. We systematically searched the literature and BioModels database reviewing systems biology (SB) and QSP models. The purpose and scope of most of these models are redundant with only two SB models serving as the basis for QSP models. Primarily three QSP models have a comprehensive scope and are systematically linked between SB and more recent QSP models. The biological scope of recent QSP models has expanded to enable simulations of previously unexplainable clotting events and the drug effects for treating bleeding or thrombosis. Overall, the field of coagulation appears to suffer from unclear connections between models and irreproducible code as previously reported. The reusability of future QSP models can improve by adopting model equations from validated QSP models, clearly documenting the purpose and modifications, and sharing reproducible code. The capabilities of future QSP models can improve from more rigorous validation by capturing a broader range of responses to therapies from individual patient measurements and integrating blood flow and platelet dynamics to closely represent in vivo bleeding or thrombosis risk.

A quantitative systems pharmacology (QSP) model of oncolytic immunotherapies based on the myxoma virus (MYXV) platform was constructed to project systemic cytokine exposure after intravenous (IV) administration, and to determine safe doses. Oncolytic viruses selectively replicate in and lyse tumor cells and provide stimulation to the immune system. The genome of MYXV is relatively large and is amenable to engineering for expression of transgenic proteins. The QSP model mechanistically describes virus administration, infection of competent cells, promoter-dependent gene transcription, cytokine payload production and secretion, and secondary cytokine responses. An in vitro model was calibrated to cytokine release data from virus infected PBMCs. A mouse model was developed to recapitulate virus PK data and cytokine data collected in the serum of tumor-bearing mice who received various IV doses of oncolytic virus constructs. The cytokine data include time-resolved measurements of transgenic cytokine expression (the viral payload), the immediate endogenous cytokine response to the virus vehicle itself, as well as secondary cytokine responses. Learnings from the in vitro model, the in vivo mouse model as well as from human specific literature information were used to create the human model. The resulting human model was used to project systemic expression of cytokine payloads and subsequent secondary cytokine responses for various IV doses of the oncolytic virus construct. Projected cytokine levels were compared to previously established cytokine exposure at maximum tolerated doses of IV administered cytokines to assess potential safety implications. The impact of uncertainties in model predictions stemming from variability in the available data and from the assumptions made during model building was evaluated. The upper range of predicted systemic cytokine exposure in humans is still expected to fall within known safety margins at the planned doses of oncolytic immunotherapies. In the absence of human data, QSP modeling represents a holistic approach of integrating all available knowledge and preclinical data for the purpose of human prediction. It yields valuable insight on the system-wide effects of IV oncolytic viral administration and can be expanded in the future to include assessment of efficacy in the form of tumor growth inhibition. Citation Format: A. Katharina Wilkins, Lina S. Franco, Diana H. Marcantonio, Karyn L. Sutton, Joshua F. Apgar, John M. Burke, Grant McFadden, Fei Hua, Leslie L. Sharp. Prediction of systemic cytokine exposure in human after IV administration of oncolytic myxoma virus, using quantitative systems pharmacology modeling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1919.

Development of a Quantitative Systems Pharmacology Model to Explore Hemostatic Equivalency of Antithrombin Lowering

Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease conditions to predict optimal therapeutic response. In this meta-analysis study, we review the utility of the QSP platform in drug development and therapeutic strategies based on recent publications (2019–2021). We gathered recent original QSP models and described the diversity of their applications based on therapeutic areas, methodologies, software platforms, and functionalities. The collection and investigation of these publications can assist in providing a repository of recent QSP studies to facilitate the discovery and further reusability of QSP models. Our review shows that the largest number of QSP efforts in recent years is in Immuno-Oncology. We also addressed the benefits of integrative approaches in this field by presenting the applications of Machine Learning methods for drug discovery and QSP models. Based on this meta-analysis, we discuss the advantages and limitations of QSP models and propose fields where the QSP approach constitutes a valuable interface for more investigations to tackle complex diseases and improve drug development.

We present a new approach to predict overall survival (OS) in untested oncology trial designs, which does not require the use of clinical covariates. This approach is based on generating a virtual population from a quantitative systems pharmacology (QSP) model of cancer immunology, and linking this virtual population to real patients from previous clinical trials. QSP has emerged as a dominant paradigm in recent years for investigating disease mechanisms and allowing prediction of drug effects in silico. However, survival cannot be described mechanistically and intrinsically predicted from a QSP model. We develop a weakly supervised learning approach to impute labels of OS and censorship in the virtual population. This approach requires prior matching of the virtual patients to real patients on the basis of longitudinal tumor growth curves. The idea that there exists a predictive relationship between tumor dynamics and OS was motivated by previous work on tumor growth inhibition (TGI-OS) [Claret, L. et al., J. Clin. Onc., 2013]. In contrast to the TGI-OS framework, we rely solely on simulated QSP dynamical variables for predicting survival. This allows us to derive OS predictions for trial designs different than the ones used for model development. Data from 5 clinical trials for atezolizumab in NSCLC (BIRCH, FIR, OAK, POPLAR, and IMpower110, total N = 1641) were used to link survival labels to virtual patients. 90% of the data was used for training, and 10% was held out for model validation. The imputed OS labels were used to train a log-normal accelerated failure time model on the training data, and predictions of survival in the test data were in good agreement with a Kaplan-Meier estimate of the clinical survival. The model predicted a median OS of 471 days (95% CI 453-490) compared to the observed 475 days, (95% CI 440-517). The rate of change of tumor size under treatment in the QSP model was the most predictive feature of survival, with a hazard ratio of 1.79 between 95th percentile and median of the dynamical range (95% CI 1.69-1.81). Model variables related to tumor antigens, cytotoxic cell death, and T cell dynamics were also relevant to the prediction of survival. This work provides the first example of generating and validating predictions of OS without using covariates from actual clinical trials. We intend to expand this methodology across different trial designs and different subpopulations. For example, our approach could be used to estimate hazard ratios between different treatments or combination therapies, as well as for patients grouped by PDL1 expression or by line of therapy. While this work only considered overall survival as an endpoint, our approach can potentially be extended to endpoints like progression-free-survival. Citation Format: Matthew West, Kenta Yoshida, Jiajie Yu, Vincent Lemaire. A treatment-agnostic approach to predict patient survival from virtual clinical trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1923.

BackgroundThe yeast-like fungi Pneumocystis, resides in lung alveoli and can cause a lethal infection known as Pneumocystis pneumonia (PCP) in hosts with impaired immune systems. Current therapies for PCP, such as trimethoprim-sulfamethoxazole (TMP-SMX), suffer from significant treatment failures and a multitude of serious side effects. Novel therapeutic approaches (i.e. newly developed drugs or novel combinations of available drugs) are needed to treat this potentially lethal opportunistic infection. Quantitative Systems Pharmacological (QSP) models promise to aid in the development of novel therapies by integrating available pharmacokinetic (PK) and pharmacodynamic (PD) knowledge to predict the effects of new treatment regimens.ResultsIn this work, we constructed and independently validated PK modules of a number of drugs with available pharmacokinetic data. Characterized by simple structures and well constrained parameters, these PK modules could serve as a convenient tool to summarize and predict pharmacokinetic profiles. With the currently accepted hypotheses on the life stages of Pneumocystis, we also constructed a PD module to describe the proliferation, transformation, and death of Pneumocystis. By integrating the PK module and the PD module, the QSP model was constrained with observed levels of asci and trophic forms following treatments with multiple drugs. Furthermore, the temporal dynamics of the QSP model were validated with corresponding data.ConclusionsWe developed and validated a QSP model that integrates available data and promises to facilitate the design of future therapies against PCP.

PV261 / #833Poster Topic:AS24 - SLE-TreatmentBackground/PurposeDespite numerous recent clinical trials, systemic lupus erythematosus (SLE) has an unmet need with only 2 approved biologics, while other autoimmune conditions have seen an explosion in approvals of targeted therapies. As such, the complex pathogenesis of SLE warrants ongoing efforts for novel therapeutic investigation and mechanistic insights. Quantitative systems pharmacology (QSP) modeling is becoming an integral tool of drug development through combining physiologic, mechanistic disease models with therapeutic exposure-response relationships. By generating a mathematical representation of molecular and cellular mechanisms in a disease, QSP can evaluate therapeutic effects and provide insight into linking clinical endpoints to optimal biological network targets, clinical trial design, and dosing strategies. A QSP model was developed to characterize clinical endpoint data, assess dosing strategies, and compare mechanism of action contribution to disease pathophysiology for a suite of therapies in moderate to severe SLE, including B cell Activating Factor (BAFF) inhibitors, type I interferon (IFN) inhibitors, tyrosine kinase 2 (TYK2) inhibitors, and standards of care. In this study, 2 IFN biologics: anifrolumab, acting upon the IFN receptor, and sifalimumab, acting upon IFN-α, are compared to assess model performance.MethodsAn ordinary differential equation (ODE)-based QSP model for SLE was constructed to describe the interplay of cells and biomolecules, tissue-level phenomena, and clinical endpoints. These interactions were modeled using literature-reported and internal information from in vitro/ex vivo assay. SLE Responder Index-4 (SRI4), Cutaneous Lupus Erythematosus Disease Area and Severity Index (CLASI), and swollen joint count (SJC) clinical endpoints are included to support clinical evaluation. Thales, a QSP modeling platform designed to streamline optimization of virtual patient populations (SimPops), was utilized to calibrate the model to 45 clinical trials and 29 treatment arms simultaneously, thereby capturing the variability of patients representing a broader SLE population. Anifrolumab trials were included in the model calibration data, while sifalimumab trials were withheld to be used only for model validation. Model assessment was evaluated by quantifying percentage of datapoints that fell within model confidence intervals.ResultsThe model’s calibrated SimPops captures SRI4, CLASI, and SJC profiles within 95% CIs across the various dosing levels of anifrolumab in the training dataset and successfully predicted sifalimumab drug effects in the validation dataset. Notably, the model reproduces clinical endpoint profiles for the trial placebo groups despite differing baseline patient characteristics and tapering protocols across the anifrolumab and sifalimumab trials. Furthermore, the model exhibits a greater SRI4 response at week 52 for anifrolumab when compared to sifalimumab, in agreement with the anifrolumab (NCT01438489) and sifalimumab (NCT01283139) trials. The difference in patient improvement may be attributable to the broader, systemic effects of targeting the IFN receptor as opposed to the IFN-α cytokine alone.ConclusionsAn SLE QSP model was successfully developed and optimized a SimPops that accurately captures clinical endpoint data for 2 IFN biologics. The Thales platform and QSP model framework allows for efficient addition of clinical trials, biological mechanisms, and therapeutics as new data becomes available, allowing for more robust predictions of novel therapeutics and combinations. Further, key determinants of individual patient response can be explored to identify patient subgroups that are best suited for specific therapies. Overall, the continuously evolving QSP model can serve as a foundation for SLE therapeutic development by providing a mechanistic understanding of SLE.

The goal of this collaboration was to provide early quantitative decision making guidance for the project team by developing and interrogating a quantitative systems pharmacology (QSP) model of the co-modulation inhibitory receptors PD-1 and TIM-3 in immuno-oncology. The QSP model was to: (1) provide predictions of the best-in-class profile for a PD-1 and TIM-3 dual antagonist biologic(s), (2) accelerate project timelines, (3) provide biological insights, and (4) reduce experimental costs. The QSP model was based on first principles as a system of elementary mass-action, mechanistic PKPD, ordinary differential equations. The model parameters and reactions were based on biophysics, and are interpretable. The model reactions include protein synthesis and elimination, ligand-receptor and drug-target formation and turnover, and drug administration and first order clearance. There were four versions of the model: PD-1 monospecific, TIM-3 monospecific, PD-1 x TIM-3 bispecific and fixed dose combination (FDC) targeting PD-1 and TIM-3. The monospecific models were then benchmarked against published data such that model parameter values were set to known values and unknown parameters were estimated. Once benchmarked, the FDC and bispecific models were analyzed by systematically investigating how tuning the model parameters (e.g., affinity, avidity, dose, half-life, target expression, etc.) impacted target inhibition, and to simulate patient variability. The model was in good agreement with published clinical data from nivolumab and pembrolizumab, and data from RMT3-23 in the TIM-3 driven mouse model. QSP model analysis predicted: (1) there would be diminishing returns on very tight binding biologics due to Target Mediated Drug Disposition (TMDD) that offsets potency, and (2) there is no advantage between FDC, 2-2 bispecific, and 2-1 bispecific formats, which are predicted to be roughly equivalent. As a result of these analyses, there was a significant reduction in the number of experiments, and acceleration of project timelines by (1) eliminating rounds of affinity maturation, as drug leads were in predicted optimal drug parameter ranges, and (2) eliminating the need to construct and evaluate bi-specific constructs and proceed with FDCs. Citation Format: Joshua F. Apgar, Jamie Wong, Ryan Phennicie, Mike Briskin, John M. Burke. Quantitative systems pharmacology and immunotherapy: accelerating lead generation and optimization of a PD-1 x TIM-3 biotherapeutic in immuno-oncology. [abstract]. In: Proceedings of the Fourth AACR International Conference on Frontiers in Basic Cancer Research; 2015 Oct 23-26; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2016;76(3 Suppl):Abstract nr B34.