Dose Optimization in Oncology Drug Development: An International Consortium for Innovation and Quality in Pharmaceutical Development White Paper.

The landscape of oncology drug development has witnessed remarkable advancements over the last few decades, significantly improving clinical outcomes and quality of life for patients with cancer. Project Optimus, introduced by the U.S. Food and Drug Administration, stands as a groundbreaking endeavor to reform dose selection of oncology drugs, presenting both opportunities and challenges for the field. To address complex dose optimization challenges, an Oncology Dose Optimization IQ Working Group was created to characterize current practices, provide recommendations for improvement, develop a clinical toolkit, and engage Health Authorities. Historically, dose selection for cytotoxic chemotherapeutics has focused on the maximum tolerated dose, a paradigm that is less relevant for targeted therapies and new treatment modalities. A survey conducted by this group gathered insights from member companies regarding industry practices in oncology dose optimization. Given oncology drug development is a complex effort with multidimensional optimization and high failure rates due to lack of clinically relevant efficacy, this Working Group advocates for a case-by-case approach to inform the timing, specific quantitative targets, and strategies for dose optimization, depending on factors such as disease characteristics, patient population, mechanism of action, including associated resistance mechanisms, and therapeutic index. This white paper highlights the evolving nature of oncology dose optimization, the impact of Project Optimus, and the need for a tailored and evidence-based approach to optimize oncology drug dosing regimens effectively.

"Digital twins elucidate critical role of Tscm in clinical persistence of TCR-engineered cell therapy".

Despite recent progress in adoptive T cell therapy for cancer, understanding and predicting the kinetics of infused T cells remains a challenge. Multiple factors can impact the distribution, expansion, and decay or persistence of infused T cells in patients. We have developed a novel quantitative systems pharmacology (QSP) model of TCR-transgenic T cell therapy in patients with solid tumors to describe the kinetics of endogenous T cells and multiple memory subsets of engineered T cells after infusion. These T cells undergo lymphodepletion, proliferation, trafficking, differentiation, and apoptosis in blood, lymph nodes, tumor site, and other peripheral tissues. Using the model, we generated patient-matched digital twins that recapitulate the circulating T cell kinetics reported from a clinical trial of TCR-engineered T cells targeting E7 in patients with metastatic HPV-associated epithelial cancers. Analyses of key parameters influencing cell kinetics and differences among digital twins identify stem cell-like memory T cells (Tscm) cells as an important determinant of both expansion and persistence and suggest that Tscm-related differences contribute significantly to the observed variability in cellular kinetics among patients. We simulated in silico clinical trials using digital twins and predict that Tscm enrichment in the infused product improves persistence of the engineered T cells and could enable administration of a lower dose. Finally, we verified the broader relevance of the QSP model, the digital twins, and findings on the importance of Tscm enrichment by predicting kinetics for two patients with pancreatic cancer treated with KRAS G12D targeting T cell therapy. This work offers insight into the key role of Tscm biology on T cell kinetics and provides a quantitative framework to evaluate cellular kinetics for future efforts in the development and clinical application of TCR-engineered T cell therapies.

Complex PK-PD of an engineered IL-15/IL-15Rα-Fc fusion protein in cynomolgus monkeys: QSP modeling of lymphocyte dynamics.

XmAb24306 is a lymphoproliferative interleukin (IL)-15/IL-15 receptor α (IL-15Rα) Fc-fusion protein currently under clinical investigation as an immunotherapeutic agent for cancer treatment. XmAb24306 contains mutations in IL-15 that attenuate its affinity to the heterodimeric IL-15 receptor ßγ (IL-15R). We observe substantially prolonged pharmacokinetics (PK) (half-life ∼ 2.5 to 4.5 days) in single- and repeat-dose cynomolgus monkey (cyno) studies compared to wild-type IL-15 (half-life ∼ 1 hour), leading to increased exposure and enhanced and durable expansion of NK cells, CD8+ T cells and CD4-CD8- (double negative [DN]) T cells. Drug clearance varied with dose level and time post-dose, and PK exposure decreased upon repeated dosing, which we attribute to increased target-mediated drug disposition (TMDD) resulting from drug-induced lymphocyte expansion (i.e., pharmacodynamic (PD)-enhanced TMDD). We developed a quantitative systems pharmacology (QSP) model to quantify the complex PKPD behaviors due to the interactions of XmAb24306 with multiple cell types (CD8+, CD4+, DN T cells, and NK cells) in the peripheral blood (PB) and lymphoid tissues. The model, which includes nonspecific drug clearance, binding to and TMDD by IL15R differentially expressed on lymphocyte subsets, and resultant lymphocyte margination/migration out of PB, expansion in lymphoid tissues, and redistribution to the blood, successfully describes the systemic PK and lymphocyte kinetics observed in the cyno studies. Results suggest that after 3 doses of every-two-week (Q2W) doses up to 70 days, the relative contributions of each elimination pathway to XmAb24306 clearance are: DN T cells > NK cells > CD8+ T cells > nonspecific clearance > CD4+ T cells. Modeling suggests that observed cellular expansion in blood results from the influx of cells expanded by the drug in lymphoid tissues. The model is used to predict lymphoid tissue expansion and to simulate PK-PD for different dose regimens. Thus, the model provides insight into the mechanisms underlying the observed PK-PD behavior of an engineered cytokine and can serve as a framework for the rapid integration and analysis of data that emerges from ongoing clinical studies in cancer patients as single-agent or given in combination.

Systems-based digital twins to help characterize clinical dose-response and propose predictive biomarkers in a Phase I study of bispecific antibody, mosunetuzumab, in NHL.

Phase I oncology clinical trials often comprise a limited number of patients representing different disease subtypes who are divided into cohorts receiving treatment(s) at different dosing levels and schedules. Here, we leverage a previously developed quantitative systems pharmacology model of the anti-CD20/CD3 T-cell engaging bispecific antibody, mosunetuzumab, to account for different dosing regimens and patient heterogeneity in the phase I study to inform clinical dose/exposure-response relationships and to identify biological determinants of clinical response. We developed a novel workflow to generate digital twins for each patient, which together form a virtual population (VPOP) that represented variability in biological, pharmacological, and tumor-related parameters from the phase I trial. Simulations based on the VPOP predict that an increase in mosunetuzumab exposure increases the proportion of digital twins with at least a 50% reduction in tumor size by day 42. Simulations also predict a left-shift of the exposure-response in patients diagnosed with indolent compared to aggressive non-Hodgkin's lymphoma (NHL) subtype; this increased sensitivity in indolent NHL was attributed to the lower inferred values of tumor proliferation rate and baseline T-cell infiltration in the corresponding digital twins. Notably, the inferred digital twin parameters from clinical responders and nonresponders show that the potential biological difference that can influence response include tumor parameters (tumor size, proliferation rate, and baseline T-cell infiltration) and parameters defining the effect of mosunetuzumab on T-cell activation and B-cell killing. Finally, the model simulations suggest intratumor expansion of pre-existing T-cells, rather than an influx of systemically expanded T-cells, underlies the antitumor activity of mosunetuzumab.

A mechanistic PK/PD model to enable dose selection of the potent anti-tryptase antibody (MTPS9579A) in patients with moderate-to-severe asthma.

Tryptase, a protease implicated in asthma pathology, is secreted from mast cells upon activation during an inflammatory allergic response. MTPS9579A is a novel monoclonal antibody that inhibits tryptase activity by irreversibly dissociating the active tetramer into inactive monomers. This study assessed the relationship between MTPS9579A concentrations in healthy subjects and tryptase levels in serum and nasal mucosal lining fluid from healthy subjects and patients with moderate-to-severe asthma. These data were used to develop a mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model that quantitatively inter-relates MTPS9579A exposure and inhibition of active tryptase in the airway of patients with asthma. From initial estimates of airway tryptase levels and drug partitioning, the PK/PD model predicted almost complete neutralization of active tryptase in the airway of patients with asthma with MTPS9579A doses of 900 mg and greater, administered intravenously (i.v.) once every 4 weeks (q4w). Suppression of active tryptase during an asthma exacerbation event was also evaluated using the model by simulating the administration of MTPS9579A during a 100-fold increase in tryptase secretion in the local tissue. The PK/PD model predicted that 1800 mg MTPS9579A i.v. q4w results in 95.7% suppression of active tryptase at the steady-state trough concentration. Understanding how the exposure-response relationship of MTPS9579A in healthy subjects translates to patients with asthma is critical for future clinical studies assessing tryptase inhibition in the airway of patients with moderate-to-severe asthma.

The convergence of pharmacometrics and quantitative systems pharmacology in pharmaceutical research and development.

Quantitative systems pharmacology (QSP) models are an important facet of pharmaceutical and clinical research as they combine mechanistic models of physiology in health and disease with pharmacokinetics/pharmacodynamics to predict systems-level effects. The quantitative clinical pharmacology toolbox has traditionally included both mechanistic modeling and population approaches, collectively called pharmacometrics, but the current landscape requires the optimization and use of multiple models together. Here, we explore several case studies in drug development that exemplify three approaches for using QSP and pharmacometrics models together - parallel synchronization, cross-informative use, and sequential integration. While these approaches are increasingly applied in drug development, achieving a true convergence of QSP and pharmacometrics that fully exploits their synergy will require new tools and methods that enable greater technical integration, in addition to nurturing scientists with diverse modeling expertise that enable cross-discipline strategy. Extensions of existing methods used in each approach as well as additional resources including machine learning models, data-at-scale, end-to-end computation platforms, and real-time analytics will enable this convergence.

Quantitative systems pharmacology model of the amyloid pathway in Alzheimer's disease: Insights into the therapeutic mechanisms of clinical candidates.

Despite considerable investment into potential therapeutic approaches for Alzheimer's disease (AD), currently approved treatment options are limited. Predictive modeling using quantitative systems pharmacology (QSP) can be used to guide the design of clinical trials in AD. This study developed a QSP model representing amyloid beta (Aß) pathophysiology in AD. The model included mechanisms of Aß monomer production and aggregation to form insoluble fibrils and plaques; the transport of soluble species between the compartments of brain, cerebrospinal fluid (CSF), and plasma; and the pharmacokinetics, transport, and binding of monoclonal antibodies to targets in the three compartments. Ordinary differential equations were used to describe these processes quantitatively. The model components were calibrated to data from the literature and internal studies, including quantitative data supporting the underlying AD biology and clinical data from clinical trials for anti-Aß monoclonal antibodies (mAbs) aducanumab, crenezumab, gantenerumab, and solanezumab. The model was developed for an apolipoprotein E (APOE) ɛ4 allele carrier and tested for an APOE ɛ4 noncarrier. Results indicate that the model is consistent with data on clinical Aß accumulation in untreated individuals and those treated with monoclonal antibodies, capturing increases in Aß load accurately. This model may be used to investigate additional AD mechanisms and their impact on biomarkers, as well as predict Aß load at different dose levels for mAbs with known targets and binding affinities. This model may facilitate the design of scientifically enriched and efficient clinical trials by enabling a priori prediction of biomarker dynamics in the brain and CSF.

Integrated systems modeling of severe asthma: Exploration of IL-33/ST2 antagonism.

Asthma is a complex, heterogeneous disease with a high unmet medical need, despite therapies targeting a multitude of pathways. The ability to quantitatively integrate preclinical and clinical data on these pathways could aid in the development and testing of novel targets and therapeutics. In this work, we develop a computational model of asthma biology, including key cell types and mediators, and create a virtual population capturing clinical heterogeneity. The simulated responses to therapies targeting IL-13, IL-4Rα, IL-5, IgE, and TSLP demonstrate agreement with clinical endpoints and biomarkers of type 2 (T2) inflammation, including blood eosinophils, FEV1, IgE, and FeNO. We use the model to explore the potential benefit of targeting the IL-33 pathway with anti-IL-33 and anti-ST2. Model predictions are compared with data on blood eosinophils, FeNO, and FEV1 from recent anti-IL-33 and anti-ST2 trials and used to interpret trial results based on pathway biology and pharmacology. Results of sensitivity analyses on the contributions of IL-33 to the predicted biomarker changes suggest that anti-ST2 therapy reduces circulating blood eosinophil levels primarily through its impact on eosinophil progenitor maturation and IL-5-dependent survival, and induces changes in FeNO and FEV1 through its effect on immune cells involved in T2 cytokine production. Finally, we also investigate the impact of ST2 genetics on the conferred benefit of anti-ST2. The model includes representation of a wide array of biologic mechanisms and interventions that will provide mechanistic insight and support clinical program design for a wide range of novel therapies during drug development.

Navigating Between Right, Wrong, and Relevant: The Use of Mathematical Modeling in Preclinical Decision Making.

The goal of this mini-review is to summarize the collective experience of the authors for how modeling and simulation approaches have been used to inform various decision points from discovery to First-In-Human clinical trials. The article is divided into a high-level overview of the types of problems that are being aided by modeling and simulation approaches, followed by detailed case studies around drug design (Nektar Therapeutics, Genentech), feasibility analysis (Novartis Pharmaceuticals), improvement of preclinical drug design (Pfizer), and preclinical to clinical extrapolation (Merck, Takeda, and Amgen).

Quantitative systems pharmacology model-based investigation of adverse gastrointestinal events associated with prolonged treatment with PI3-kinase inhibitors.

Several PI3K inhibitors are in clinical development for the treatment of various forms of cancers, including pan-PI3K inhibitors targeting all four PI3K isoforms (α, ß, γ, and δ), and isoform-selective inhibitors. Diarrhea and immune-mediated colitis are among the adverse events observed with PI3K inhibition which limits the maximal tolerated dose. A quantitative systems pharmacology model was developed to investigate PI3K-inhibitor-induced colitis. The effects of individual PI3K isoforms on relevant cellular pathways were incorporated into a mechanistic representation of mucosal inflammation. A virtual clinical population captures the observed clinical variability in the onset timing and rates of diarrhea and colitis for seven clinically tested PI3K inhibitors. Model-based analysis suggests that colitis development is governed by both the inhibition of PI3Kδ, which drives T cell differentiation and proliferation, and PI3Kα, which regulates epithelial barrier integrity. Specifically, when PI3Kα is inhibited below a given threshold, epithelial barrier dysfunction precipitates an exaggerated T effector response due to PI3Kδ-inhibition, leading to risk of diarrhea and colitis. This synergy explains why the lowest diarrhea and colitis rates are seen with the weakest PI3Kδ inhibition (alpelisib), and higher rates are seen with strong PI3Kδ inhibition if PI3Kα is even mildly inhibited (e.g., idelalisib), whereas strong PI3Kδ inhibition in the absence of PI3Kα inhibition does not result in high colitis rates (umbralisib). Thus, the model-based analysis suggests that PI3Kα and δ inhibition play unique but synergistic roles in driving colitis. Finally, we explore if and how dose-regimen might influence colitis rates for molecules that inhibit both PI3Kα and PI3Kδ.

Mitigating the risk of cytokine release syndrome in a Phase I trial of CD20/CD3 bispecific antibody mosunetuzumab in NHL: impact of translational system modeling.

Mosunetuzumab, a T-cell dependent bispecific antibody that binds CD3 and CD20 to drive T-cell mediated B-cell killing, is currently being tested in non-Hodgkin lymphoma. However, potent immune stimulation with T-cell directed therapies poses the risk of cytokine release syndrome, potentially limiting dose and utility. To understand mechanisms behind safety and efficacy and explore safety mitigation strategies, we developed a novel mechanistic model of immune and antitumor responses to the T-cell bispecifics (mosunetuzumab and blinatumomab), including the dynamics of B- and T-lymphocytes in circulation, lymphoid tissues, and tumor. The model was developed and validated using mosunetuzumab nonclinical and blinatumomab clinical data. Simulations delineated mechanisms contributing to observed cell and cytokine (IL6) dynamics and predicted that initial step-fractionated dosing limits systemic T-cell activation and cytokine release without compromising tumor response. These results supported a change to a step-fractionated treatment schedule of mosunetuzumab in the ongoing Phase I clinical trial, enabling safer administration of higher doses.

Mechanistic Modeling of Soluble Aß Dynamics and Target Engagement in the Brain by Anti-Aß mAbs in Alzheimer's Disease.

BACKGROUND: Anti-amyloid-ß (Aß) monoclonal antibodies (mAbs) are currently in development for treating Alzheimer's disease. OBJECTIVES: To address the complexity of Aß target engagement profiles, improve the understanding of crenezumab Pharmacokinetics (PK) and Aß Pharmacodynamics (PD) in the brain, and facilitate comparison of anti-Aß therapies with different binding characteristics. METHODS: A mechanistic mathematical model was developed describing the distribution, elimination, and binding kinetics of anti-Aß mAbs and Aß (monomeric and oligomeric forms of Aß1-40 and Aß1-42) in the brain, Cerebrospinal Fluid (CSF), and plasma. Physiologically meaningful values were assigned to the model parameters based on the previous data, with remaining parameters fitted to clinical measurements of Aß concentrations in CSF and plasma, and PK/PD data of patients undergoing anti-Aß therapy. Aß target engagement profiles were simulated using a Monte Carlo approach to explore the impact of biological uncertainty in the model parameters. RESULTS: Model-based estimates of in vivo affinity of the antibody to monomeric Aß were qualitatively consistent with the previous data. Simulations of Aß target engagement profiles captured observed mean and variance of clinical PK/PD data. CONCLUSION: This model is useful for comparing target engagement profiles of different anti-Aß therapies and demonstrates that 60 mg/kg crenezumab yields a significant increase in Aß engagement compared with lower doses of solanezumab, supporting the selection of 60 mg/kg crenezumab for phase 3 studies. The model also provides evidence that the delivery of sufficient quantities of mAb to brain interstitial fluid is a limiting step with respect to the magnitude of soluble Aß oligomer neutralization.

gQSPSim: A SimBiology-Based GUI for Standardized QSP Model Development and Application.

Quantitative systems pharmacology (QSP) models are often implemented using a wide variety of technical workflows and methodologies. To facilitate reproducibility, transparency, portability, and reuse for QSP models, we have developed gQSPSim, a graphical user interface-based MATLAB application that performs key steps in QSP model development and analyses. The capabilities of gQSPSim include (i) model calibration using global and local optimization methods, (ii) development of virtual subjects to explore variability and uncertainty in the represented biology, and (iii) simulations of virtual populations for different interventions. gQSPSim works with SimBiology-built models using components such as species, doses, variants, and rules. All functionalities are equipped with an interactive visualization interface and the ability to generate presentation-ready figures. In addition, standardized gQSPSim sessions can be shared and saved for future extension and reuse. In this work, we demonstrate gQSPSim's capabilities with a standard target-mediated drug disposition model and a published model of anti-proprotein convertase subtilisin/kexin type 9 (PCSK9) treatment of hypercholesterolemia.

Preclinical and translational pharmacokinetics of a novel THIOMAB™ antibody-antibiotic conjugate against Staphylococcus aureus.

DSTA4637S, a novel THIOMAB™ antibody-antibiotic conjugate (TAC) against Staphylococcus aureus (S. aureus), is currently being investigated as a potential therapy for complicated S. aureus bloodstream infections. DSTA4637S is composed of a monoclonal THIOMABTM IgG1 recognizing S. aureus linked to a rifamycin-class antibiotic (dmDNA31) via a protease-cleavable linker. The pharmacokinetics (PK) of DSTA4637A (a liquid formulation of DSTA4637S) and its unconjugated antibody MSTA3852A were characterized in rats and monkeys. Systemic concentrations of three analytes, total antibody (TAb), antibody-conjugated dmDNA31 (ac-dmDNA31), and unconjugated dmDNA31, were measured to describe complex TAC PK in nonclinical studies. In rats and monkeys, following intravenous administration of a single dose of DSTA4637A, systemic concentration-time profiles of both TAb and ac-dmDNA31 were bi-exponential, characterized by a short distribution phase and a long elimination phase as expected for a monoclonal antibody-based therapeutic. Systemic exposures of both TAb and ac-dmDNA31 were dose proportional over the dose range tested, and ac-dmDNA31 cleared 2-3 times faster than TAb. Unconjugated dmDNA31 plasma concentrations were low (<4 ng/mL) in every study regardless of dose. In this report, an integrated semi-mechanistic PK model for two analytes (TAb and ac-dmDNA31) was successfully developed and was able to well describe the complicated DSTA4637A PK in mice, rats and monkeys. DSTA4637S human PK was predicted reasonably well using this model with allometric scaling of PK parameters from monkey data. This work provides insights into PK behaviors of DSTA4637A in preclinical species and informs clinical translatability of these observed results and further clinical development. Abbreviations: ADC: Antibody-drug conjugate; AUCinf: time curve extrapolated to infinity; ac-dmDNA31: antibody-conjugated dmDNA31; Cmax: maximum concentration observed; DAR: drug-to-antibody ratio; CL: clearance; CLD: distribution clearance; CL1: systemic clearance of all DAR species; kDC: deconjugation rate constant; PK: Pharmacokinetics; IV: Intravenous; IgG: Immunoglobulin G; mAb: monoclonal antibody; S. aureus: Staphylococcus aureus; TAC: THIOMABTM antibody-antibiotic conjugate; TDC: THIOMABTM antibody-drug conjugate; TAb: total antibody; t1/2, λz: terminal half-life; vc linker: valine-citrulline linker; Vss: volume of distribution at steady state; Vc: volume of distribution for the central compartment; Vp: the volume of distribution for the peripheral compartment.

A transcriptional MAPK Pathway Activity Score (MPAS) is a clinically relevant biomarker in multiple cancer types.

KRAS- and BRAF-mutant tumors are often dependent on MAPK signaling for proliferation and survival and thus sensitive to MAPK pathway inhibitors. However, clinical studies have shown that MEK inhibitors are not uniformly effective in these cancers indicating that mutational status of these oncogenes does not accurately capture MAPK pathway activity. A number of transcripts are regulated by this pathway and are recurrently identified in genome-based MAPK transcriptional signatures. To test whether the transcriptional output of only 10 of these targets could quantify MAPK pathway activity with potential predictive or prognostic clinical utility, we created a MAPK Pathway Activity Score (MPAS) derived from aggregated gene expression. In vitro, MPAS predicted sensitivity to MAPK inhibitors in multiple cell lines, comparable to or better than larger genome-based statistical models. Bridging in vitro studies and clinical samples, median MPAS from a given tumor type correlated with cobimetinib (MEK inhibitor) sensitivity of cancer cell lines originating from the same tissue type. Retrospective analyses of clinical datasets showed that MPAS was associated with the sensitivity of melanomas to vemurafenib (HR: 0.596) and negatively prognostic of overall or progression-free survival in both adjuvant and metastatic CRC (HR: 1.5 and 1.4), adrenal cancer (HR: 1.7), and HER2+ breast cancer (HR: 1.6). MPAS thus demonstrates potential clinical utility that warrants further exploration.

A Preclinical Population Pharmacokinetic Model for Anti-CD20/CD3 T-Cell-Dependent Bispecific Antibodies.

CD20 is a cell-surface receptor expressed by healthy and neoplastic B cells and is a well-established target for biologics used to treat B-cell malignancies. Pharmacokinetic (PK) and pharmacodynamic (PD) data for the anti-CD20/CD3 T-cell-dependent bispecific antibody BTCT4465A were collected in transgenic mouse and nonhuman primate (NHP) studies. Pronounced nonlinearity in drug elimination was observed in the murine studies, and time-varying, nonlinear PK was observed in NHPs, where three empirical drug elimination terms were identified using a mixed-effects modeling approach: i) a constant nonsaturable linear clearance term (7 mL/day/kg); ii) a rapidly decaying time-varying, linear clearance term (t½  = 1.6 h); and iii) a slowly decaying time-varying, nonlinear clearance term (t½  = 4.8 days). The two time-varying drug elimination terms approximately track with time scales of B-cell depletion and T-cell migration/expansion within the central blood compartment. The mixed-effects NHP model was scaled to human and prospective clinical simulations were generated.

gPKPDSim: a SimBiology®-based GUI application for PKPD modeling in drug development.

Modeling and simulation (M&S) is increasingly used in drug development to characterize pharmacokinetic-pharmacodynamic (PKPD) relationships and support various efforts such as target feasibility assessment, molecule selection, human PK projection, and preclinical and clinical dose and schedule determination. While model development typically require mathematical modeling expertise, model exploration and simulations could in many cases be performed by scientists in various disciplines to support the design, analysis and interpretation of experimental studies. To this end, we have developed a versatile graphical user interface (GUI) application to enable easy use of any model constructed in SimBiology® to execute various common PKPD analyses. The MATLAB®-based GUI application, called gPKPDSim, has a single screen interface and provides functionalities including simulation, data fitting (parameter estimation), population simulation (exploring the impact of parameter variability on the outputs of interest), and non-compartmental PK analysis. Further, gPKPDSim is a user-friendly tool with capabilities including interactive visualization, exporting of results and generation of presentation-ready figures. gPKPDSim was designed primarily for use in preclinical and translational drug development, although broader applications exist. gPKPDSim is a MATLAB®-based open-source application and is publicly available to download from MATLAB® Central™. We illustrate the use and features of gPKPDSim using multiple PKPD models to demonstrate the wide applications of this tool in pharmaceutical sciences. Overall, gPKPDSim provides an integrated, multi-purpose user-friendly GUI application to enable efficient use of PKPD models by scientists from various disciplines, regardless of their modeling expertise.

Erratum: Clinical responses to ERK inhibition in BRAFV600E-mutant colorectal cancer predicted using a computational model.

[This corrects the article DOI: 10.1038/s41540-017-0016-1.].

Clinical responses to ERK inhibition in BRAFV600E-mutant colorectal cancer predicted using a computational model.

Approximately 10% of colorectal cancers harbor BRAFV600E mutations, which constitutively activate the MAPK signaling pathway. We sought to determine whether ERK inhibitor (GDC-0994)-containing regimens may be of clinical benefit to these patients based on data from in vitro (cell line) and in vivo (cell- and patient-derived xenograft) studies of cetuximab (EGFR), vemurafenib (BRAF), cobimetinib (MEK), and GDC-0994 (ERK) combinations. Preclinical data was used to develop a mechanism-based computational model linking cell surface receptor (EGFR) activation, the MAPK signaling pathway, and tumor growth. Clinical predictions of anti-tumor activity were enabled by the use of tumor response data from three Phase 1 clinical trials testing combinations of EGFR, BRAF, and MEK inhibitors. Simulated responses to GDC-0994 monotherapy (overall response rate = 17%) accurately predicted results from a Phase 1 clinical trial regarding the number of responding patients (2/18) and the distribution of tumor size changes ("waterfall plot"). Prospective simulations were then used to evaluate potential drug combinations and predictive biomarkers for increasing responsiveness to MEK/ERK inhibitors in these patients.