Implementation of quantitative and systems pharmacology in large pharma

The Role of Cross-Institutional and Interdisciplinary Collaboration in Defining and Executing a Quantitative Systems Pharmacology Strategy

The application of quantitative systems pharmacology (QSP) has enabled substantial progress and impact in many areas of therapeutic discovery and development. This new technology is increasingly accepted by industry, academia, and solution providers, and is enjoying greater interest from regulators. In this chapter, we summarize key aspects regarding how effective collaboration among institutions and disciplines can support the growth of QSP and expand its application domain. We exemplify these considerations through a selection of successful cross-institutional or cross-functional collaborations, which resulted in reuse, repurposing, or extension of QSP modeling results or infrastructure, with important and novel results.

Recommendations for a standardized publication protocol for a QSP model

Development of a Quantitative Systems Pharmacology (QSP) model is a long process with many iterative steps. Lack of standard practices for publishing QSP models has resulted in limited model reproducibility within the field. Multiple studies have identified that model reproducibility is a large challenge, especially for QSP models. This work aimed to investigate the causes of QSP model reproducibility issues and suggest standard practices as a potential solution to ensure QSP models are reproducible. In addition, a protocol is suggested as a guidance towards better publication strategy across journals, hoping to enable QSP knowledge preservation.

Comparison of drug-induced liver injury risk between propylthiouracil and methimazole: A quantitative systems toxicology approach

Propylthiouracil (PTU) and methimazole (MMI), two classical antithyroid agents possess risk of drug-induced liver injury (DILI) with unknown mechanism of action. This study aimed to examine and compare their hepatic toxicity using a quantitative system toxicology approach. The impact of PTU and MMI on hepatocyte survival, oxidative stress, mitochondrial function and bile acid transporters were assessed in vitro. The physiologically based pharmacokinetic (PBPK) models of PTU and MMI were constructed while their risk of DILI was calculated by DILIsym, a quantitative systems toxicology (QST) model by integrating the results from in vitro toxicological studies and PBPK models. The simulated DILI (ALT >2 × ULN) incidence for PTU (300 mg/d) was 21.2%, which was within the range observed in clinical practice. Moreover, a threshold dose of 200 mg/d was predicted with oxidative stress proposed as an important toxic mechanism. However, DILIsym predicted a 0% incidence of hepatoxicity caused by MMI (30 mg/d), suggesting that the toxicity of MMI was not mediated through mechanism incorporated into DILIsym. In conclusion, DILIsym appears to be a practical tool to unveil hepatoxicity mechanism and predict clinical risk of DILI.

Real-World Application of a Quantitative Systems Pharmacology (QSP) Model to Predict Potassium Concentrations from Electronic Health Records: A Pilot Case towards Prescribing Monitoring of Spironolactone

Quantitative systems pharmacology (QSP) models are rarely applied prospectively for decision-making in clinical practice. We therefore aimed to operationalize a QSP model for potas-sium homeostasis to predict potassium trajectories based on spironolactone administrations. For this purpose, we proposed a general workflow that was applied to electronic health records (EHR) from patients treated in a German tertiary care hospital. The workflow steps included model exploration, local and global sensitivity analyses (SA), identifiability analysis (IA) of model parameters, and specification of their inter-individual variability (IIV). Patient covariates, selected parameters, and IIV then defined prior information for the Bayesian a posteriori prediction of individual potassium trajectories of the following day. Following these steps, the successfully operationalized QSP model was interactively explored via a Shiny app. SA and IA yielded five influential and estimable parameters (extracellular fluid volume, hyperaldosteronism, mineral corticoid receptor abundance, potassium intake, sodium intake) for Bayesian prediction. The operationalized model was validated in nine pilot patients and showed satisfactory performance based on the (absolute) average fold error. This provides proof-of-principle for a Prescribing Monitoring of potassium concentrations in a hospital system, which could suggest preemptive clinical measures and therefore potentially avoid dangerous hyperkalemia or hypokalemia.

Applying quantitative and systems pharmacology to drug development and beyond: An introduction to clinical pharmacologists

ABSTRACT

Quantitative and systems pharmacology (QSP) is an innovative and integrative approach combining physiology and pharmacology to accelerate medical research. This review focuses on QSP's pivotal role in drug development and its broader applications, introducing clinical pharmacologists/researchers to QSP's quantitative approach and the potential to enhance their practice and decision-making. The history of QSP adoption reveals its impact in diverse areas, including glucose regulation, oncology, autoimmune disease, and HIV treatment. By considering receptor-ligand interactions of various cell types, metabolic pathways, signaling networks, and disease biomarkers simultaneously, QSP provides a holistic understanding of interactions between the human body, diseases, and drugs. Integrating knowledge across multiple time and space scales enhances versatility, enabling insights into personalized responses and general trends. QSP consolidates vast data into robust mathematical models, predicting clinical trial outcomes and optimizing dosing based on preclinical data. QSP operates under a "learn and confirm paradigm," integrating experimental findings to generate testable hypotheses and refine them through precise experimental designs. An interdisciplinary collaboration involving expertise in pharmacology, biochemistry, genetics, mathematics, and medicine is vital. QSP's utility in drug development is demonstrated through integration in various stages, predicting drug responses, optimizing dosing, and evaluating combination therapies. Challenges exist in model complexity, communication, and peer review. Standardized workflows and evaluation methods ensure reliability and transparency.

In Silico Clinical Trials: Is It Possible?

Modeling and simulation (M&S), including in silico (clinical) trials, helps accelerate drug research and development and reduce costs and have coined the term "model-informed drug development (MIDD)." Data-driven, inferential approaches are now becoming increasingly complemented by emerging complex physiologically and knowledge-based disease (and drug) models, but differ in setup, bottlenecks, data requirements, and applications (also reminiscent of the different scientific communities they arose from). At the same time, and within the MIDD landscape, regulators and drug developers start to embrace in silico trials as a potential tool to refine, reduce, and ultimately replace clinical trials. Effectively, silos between the historically distinct modeling approaches start to break down. Widespread adoption of in silico trials still needs more collaboration between different stakeholders and established precedence use cases in key applications, which is currently impeded by a shattered collection of tools and practices. In order to address these key challenges, efforts to establish best practice workflows need to be undertaken and new collaborative M&S tools devised, and an attempt to provide a coherent set of solutions is provided in this chapter. First, a dedicated workflow for in silico clinical trial (development) life cycle is provided, which takes up general ideas from the systems biology and quantitative systems pharmacology space and which implements specific steps toward regulatory qualification. Then, key characteristics of an in silico trial software platform implementation are given on the example of jinkō.ai (nova's end-to-end in silico clinical trial platform). Considering these enabling scientific and technological advances, future applications of in silico trials to refine, reduce, and replace clinical research are indicated, ranging from synthetic control strategies and digital twins, which overall shows promise to begin a new era of more efficient drug development.

Quantitative Systems Pharmacology and Machine Learning: A Match Made in Heaven or Hell?

As pharmaceutical development moves from early-stage in vitro experimentation to later in vivo and subsequent clinical trials, data and knowledge are acquired across multiple time and length scales, from the subcellular to whole patient cohort scale. Realizing the potential of this data for informing decision making in pharmaceutical development requires the individual and combined application of machine learning (ML) and mechanistic multiscale mathematical modeling approaches. Here we outline how these two approaches, both individually and in tandem, can be applied at different stages of the drug discovery and development pipeline to inform decision making compound development. The importance of discerning between knowledge and data are highlighted in informing the initial use of ML or mechanistic quantitative systems pharmacology (QSP) models. We discuss the application of sensitivity and structural identifiability analyses of QSP models in informing future experimental studies to which ML may be applied, as well as how ML approaches can be used to inform mechanistic model development. Relevant literature studies are highlighted and we close by discussing caveats regarding the application of each approach in an age of constant data acquisition. SIGNIFICANCE STATEMENT: We consider when best to apply machine learning (ML) and mechanistic quantitative systems pharmacology (QSP) approaches in the context of the drug discovery and development pipeline. We discuss the importance of prior knowledge and data available for the system of interest and how this informs the individual and combined application of ML and QSP approaches at each stage of the pipeline.

Modeling breast cancer proliferation, drug synergies, and alternating therapies

Estrogen receptor positive (ER+) breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of targeted therapy often results in resistance, driving the consideration of combination and alternating therapies. Toward this end, we developed a mathematical model that can simulate various mono, combination, and alternating therapies for ER + breast cancer cells at different doses over long time scales. The model is used to look for optimal drug combinations and predicts a significant synergism between Cdk4/6 inhibitors in combination with the anti-estrogen fulvestrant, which may help explain the clinical success of adding Cdk4/6 inhibitors to anti-estrogen therapy. Furthermore, the model is used to optimize an alternating treatment protocol so it works as well as monotherapy while using less total drug dose.

Mathematically Modeling the Effect of Endocrine and Cdk4/6 Inhibitor Therapies on Breast Cancer Cells

Mathematical modeling of cancer systems is beginning to be used to design better treatment regimens, especially in chemotherapy and radiotherapy. The effectiveness of mathematical modeling to inform treatment decisions and identify therapy protocols, some of which are highly nonintuitive, is because it enables the exploration of a huge number of therapeutic possibilities. Considering the immense cost of laboratory research and clinical trials, these nonintuitive therapy protocols would likely never be found by experimental approaches. While much of the work to date in this area has involved high-level models, which look simply at overall tumor growth or the interaction of resistant and sensitive cell types, mechanistic models that integrate molecular biology and pharmacology can contribute greatly to the discovery of better cancer treatment regimens. These mechanistic models are better able to account for the effect of drug interactions and the dynamics of therapy. The aim of this chapter is to demonstrate the use of ordinary differential equation-based mechanistic models to describe the dynamic interactions between the molecular signaling of breast cancer cells and two key clinical drugs. In particular, we illustrate the procedure for building a model of the response of MCF-7 cells to standard therapies used in the clinic. Such mathematical models can be used to explore the vast number of potential protocols to suggest better treatment approaches.

Quantitative system pharmacology as a legitimate approach to examine extrapolation strategies used to support pediatric drug development

Extrapolation strategies from adult data for designing pediatric drug development programs are explored using the quantitative systems pharmacology (QSP) modeling approach, a mechanistic drug and disease modeling framework that can predict clinical response and guide pediatric drug development in general. This innovative model‐informed drug discovery and development approach can leverage adult‐pediatric pharmacology and disease similarity metrics to validate extrapolation assumptions. We describe the QSP model strategy and framework for extrapolation to design pediatric drug development programs by leveraging adult data across a wide range of therapeutic areas and illustrating stage‐gate decisions informed by such an approach.

Targeting Cellular DNA Damage Responses in Cancer: An In Vitro-Calibrated Agent-Based Model Simulating Monolayer and Spheroid Treatment Responses to ATR-Inhibiting Drugs

We combine a systems pharmacology approach with an agent-based modelling approach to simulate LoVo cells subjected to AZD6738, an ATR (ataxia–telangiectasia-mutated and rad3-related kinase) inhibiting anti-cancer drug that can hinder tumour proliferation by targeting cellular DNA damage responses. The agent-based model used in this study is governed by a set of empirically observable rules. By adjusting only the rules when moving between monolayer and multi-cellular tumour spheroid simulations, whilst keeping the fundamental mathematical model and parameters intact, the agent-based model is first parameterised by monolayer in vitro data and is thereafter used to simulate treatment responses in in vitro tumour spheroids subjected to dynamic drug delivery. Spheroid simulations are subsequently compared to in vivo data from xenografts in mice. The spheroid simulations are able to capture the dynamics of in vivo tumour growth and regression for approximately 8 days post-tumour injection. Translating quantitative information between in vitro and in vivo research remains a scientifically and financially challenging step in preclinical drug development processes. However, well-developed in silico tools can be used to facilitate this in vitro to in vivo translation, and in this article, we exemplify how data-driven, agent-based models can be used to bridge the gap between in vitro and in vivo research. We further highlight how agent-based models, that are currently underutilised in pharmaceutical contexts, can be used in preclinical drug development.

Cardiorenal Systems Modeling: Left Ventricular Hypertrophy and Differential Effects of Antihypertensive Therapies on Hypertrophy Regression

Cardiac and renal function are inextricably connected through both hemodynamic and neurohormonal mechanisms, and the interaction between these organ systems plays an important role in adaptive and pathophysiologic remodeling of the heart, as well as in the response to renally acting therapies. Insufficient understanding of the integrative function or dysfunction of these physiological systems has led to many examples of unexpected or incompletely understood clinical trial results. Mathematical models of heart and kidney physiology have long been used to better understand the function of these organs, but an integrated model of renal function and cardiac function and cardiac remodeling has not yet been published. Here we describe an integrated cardiorenal model that couples existing cardiac and renal models, and expands them to simulate cardiac remodeling in response to pressure and volume overload, as well as hypertrophy regression in response to angiotensin receptor blockers and beta-blockers. The model is able to reproduce different patterns of hypertrophy in response to pressure and volume overload. We show that increases in myocyte diameter are adaptive in pressure overload not only because it normalizes wall shear stress, as others have shown before, but also because it limits excess volume accumulation and further elevation of cardiac stresses by maintaining cardiac output and renal sodium and water balance. The model also reproduces the clinically observed larger LV mass reduction with angiotensin receptor blockers than with beta blockers. We further provide a mechanistic explanation for this difference by showing that heart rate lowering with beta blockers limits the reduction in peak systolic wall stress (a key signal for myocyte hypertrophy) relative to ARBs.

Prediction of a clinically effective dose of THY1773, a novel V1B receptor antagonist, based on preclinical data

THY1773 is a novel arginine vasopressin 1B (V1B ) receptor antagonist that is under development as an oral drug for the treatment of major depressive disorder (MDD). Here we report our strategy to predict a clinically effective dose of THY1773 for MDD in the preclinical stage, and discuss the important insights gained by retrospective analysis of prediction accuracy. To predict human pharmacokinetic (PK) parameters, several extrapolation methods from animal or in vitro data to humans were investigated. The fu correction intercept method and two-species-based allometry were used to extrapolate clearance from rats and dogs to humans. The physiologically based pharmacokinetics (PBPK)/receptor occupancy (RO) model was developed by linking free plasma concentration with pituitary V1B RO by the Emax model. As a result, the predicted clinically effective dose of THY1773 associated with 50% V1B RO was low enough (10 mg/day, or at maximum 110 mg/day) to warrant entering phase 1 clinical trials. In the phase 1 single ascending dose study, TS-121 capsule (active ingredient: THY1773) showed favorable PKs for THY1773 as expected, and in the separately conducted phase 1 RO study using positron emission tomography, the observed pituitary V1B RO was comparable to our prediction. Retrospective analysis of the prediction accuracy suggested that the prediction methods considering plasma protein binding, and avoiding having to apply unknown scaling factors obtained in animals to humans, would lead to better prediction. Selecting mechanism-based methods with reasonable assumptions would be critical for the successful prediction of a clinically effective dose in the preclinical stage of drug development.

Pharmacodynamic modeling of synergistic birinapant/paclitaxel interactions in pancreatic cancer cells

Background

For most patients, pancreatic adenocarcinoma responds poorly to treatment, and novel therapeutic approaches are needed. Standard-of-care paclitaxel (PTX), combined with birinapant (BRP), a bivalent mimetic of the apoptosis antagonist SMAC (second mitochondria-derived activator of caspases), exerts synergistic killing of PANC-1 human pancreatic adenocarcinoma cells.

Methods

To investigate potential mechanisms underlying this synergistic pharmacodynamic interaction, data capturing PANC-1 cell growth, apoptosis kinetics, and cell cycle distribution were integrated with high-quality IonStar-generated proteomic data capturing changes in the relative abundance of more than 3300 proteins as the cells responded to the two drugs, alone and combined.

Results

PTX alone (15 nM) elicited dose-dependent G2/M-phase arrest and cellular polyploidy. Combined BRP/PTX (150/15 nM) reduced G2/M by 35% and polyploid cells by 45%, and increased apoptosis by 20%. Whereas BRP or PTX alone produced no change in the pro-apoptotic protein pJNK, and a slight increase in the anti-apoptotic protein Bcl2, the drug combination increased pJNK and decreased Bcl2 significantly compared to the vehicle control. A multi-scale, mechanism-based mathematical model was developed to investigate integrated birinapant/paclitaxel effects on temporal profiles of key proteins involved in kinetics of cell growth, death, and cell cycle distribution.

Conclusions

The model, consistent with the observed reduction in the Bcl2/BAX ratio, suggests that BRP-induced apoptosis of mitotically-arrested cells is a major contributor to the synergy between BRP and PTX. Coupling proteomic and cellular response profiles with multi-scale pharmacodynamic modeling provides a quantitative mechanistic framework for evaluating pharmacodynamically-based drug-drug interactions in combination chemotherapy, and could potentially guide the development of promising drug regimens.

QSP-IO: A Quantitative Systems Pharmacology Toolbox for Mechanistic Multiscale Modeling for Immuno-Oncology Applications

Immunotherapy has shown great potential in the treatment of cancer; however, only a fraction of patients respond to treatment, and many experience autoimmune‐related side effects. The pharmaceutical industry has relied on mathematical models to study the behavior of candidate drugs and more recently, complex, whole‐body, quantitative systems pharmacology (QSP) models have become increasingly popular for discovery and development. QSP modeling has the potential to discover novel predictive biomarkers as well as test the efficacy of treatment plans and combination therapies through virtual clinical trials. In this work, we present a QSP modeling platform for immuno‐oncology (IO) that incorporates detailed mechanisms for important immune interactions. This modular platform allows for the construction of QSP models of IO with varying degrees of complexity based on the research questions. Finally, we demonstrate the use of the platform through two example applications of immune checkpoint therapy.

Mechanistic Multiscale Pharmacokinetic Model for the Anticancer Drug 2',2'-difluorodeoxycytidine (Gemcitabine) in Pancreatic Cancer

The aim of this work is to build a mechanistic multiscale pharmacokinetic model for the anticancer drug 2’,2’‐difluorodeoxycytidine (gemcitabine, dFdC), able to describe the concentrations of dFdC metabolites in the pancreatic tumor tissue in dependence of physiological and genetic patient characteristics, and, more in general, to explore the capabilities and limitations of this kind of modeling strategy. A mechanistic model characterizing dFdC metabolic pathway (metabolic network) was developed using in vitro literature data from two pancreatic cancer cell lines. The network was able to describe the time course of extracellular and intracellular dFdC metabolites concentrations. Moreover, a physiologically‐based pharmacokinetic model was developed to describe clinical dFdC profiles by using enzymatic and physiological information available in the literature. This model was then coupled with the metabolic network to describe the dFdC active metabolite profile in the pancreatic tumor tissue. Finally, global sensitivity analysis was performed to identify the parameters that mainly drive the interindividual variability for the area under the curve (AUC) of dFdC in plasma and of its active metabolite (dFdCTP) in tumor tissue. From this analysis, cytidine deaminase (CDA) concentration was identified as the main driver of plasma dFdC AUC interindividual variability, whereas CDA and deoxycytidine kinase concentration mainly explained the tumor dFdCTP AUC variability. However, the lack of in vitro and in vivo information needed to characterize key model parameters hampers the development of this kind of mechanistic approach. Further studies to better characterize pancreatic cell lines and patient enzymes polymorphisms are encouraged to refine and validate the current model.

Multiscale modelling of drug transport and metabolism in liver spheroids

In early preclinical drug development, potential candidates are tested in the laboratory using isolated cells. These in vitro experiments traditionally involve cells cultured in a two-dimensional monolayer environment. However, cells cultured in three-dimensional spheroid systems have been shown to more closely resemble the functionality and morphology of cells in vivo. While the increasing usage of hepatic spheroid cultures allows for more relevant experimentation in a more realistic biological environment, the underlying physical processes of drug transport, uptake and metabolism contributing to the spatial distribution of drugs in these spheroids remain poorly understood. The development of a multiscale mathematical modelling framework describing the spatio-temporal dynamics of drugs in multicellular environments enables mechanistic insight into the behaviour of these systems. Here, our analysis of cell membrane permeation and porosity throughout the spheroid reveals the impact of these properties on drug penetration, with maximal disparity between zonal metabolism rates occurring for drugs of intermediate lipophilicity. Our research shows how mathematical models can be used to simulate the activity and transport of drugs in hepatic spheroids and in principle any organoid, with the ultimate aim of better informing experimentalists on how to regulate dosing and culture conditions to more effectively optimize drug delivery.

A Model-Informed Drug Discovery and Development Strategy for the Novel Glucose-Responsive Insulin MK-2640 Enabled Rapid Decision Making

A model‐informed drug discovery and development strategy played a key role in the novel glucose‐responsive insulin MK‐2640’s early clinical development strategy and supported a novel clinical trial paradigm to assess glucose responsiveness. The development and application of in silico modeling approaches by leveraging substantial published clinical insulin pharmacokinetic–pharmacodynamic (PKPD) data and emerging preclinical and clinical data enabled rapid quantitative decision making. Learnings can be applied to define PKPD properties of novel insulins that could become therapeutically meaningful for diabetic patients.

Application and Impact of Human Dose Projection from Discovery to Early Drug Development

The application and impact of human dose projection (HDP) has been well recognized in the late drug development phase, with increasing appreciation earlier during discovery and early development. This commentary describes the perspective of pharmaceutical scientists on the evolving application and impact of HDP at various phases from discovery to early development, including lead generation, lead optimization, lead up to candidate nomination, and early drug development. The underlying fundamental concepts and key input parameters for HDP are briefly discussed. A broad overview of phase-specific tools and approaches commonly utilized for human dose projection in the pharmaceutical industry is provided. A discussion of phase-appropriate implementation strategies, associated limitations/assumptions and continuous refinement for HDP from discovery to early development is presented. The authors describe the phase-specific applications of human dose projection to facilitate key assessments and relative impact on decision points.

GAPPS (Grading and Assessment of Pharmacokinetic-Pharmacodynamic Studies) a critical appraisal system for antimicrobial PKPD studies - development and application in pediatric antibiotic studies

Introduction: There are limited data on optimal dosing of antibiotics in different age groups for neonates and children. Clinicians usually consult pediatric formularies or online databases for dose selection, but these have variable recommendations, are usually based on expert opinion and are not graded based on the existing pharmacokinetic-pharmacodynamic (PKPD) studies. We describe here a potential new tool that could be used to grade the strength of evidence emanating from PKPD studies.Areas covered: A scoring system was developed (GAPPS tool) to quantify the strength of each PK assessment and rate the studies quality in already published articles. GAPPS was evaluated by applying it to pediatric PKPD studies of antibiotics from the 2019 Essential Medicines List for children (EML_C), identified through a search of PubMed.Expert opinion: Evidence for most antibiotic dose selection decisions was generally weak, coming from individual PK studies and lacked PKPD modeling and simulations. However, the quality of evidence appears to have improved over the last two decades.Incorporating a formal grading system, such as GAPPS, into formulary development will provide a transparent tool to support decision-making in clinical practice and guideline development, and guide PKPD authors on study designs most likely to influence guidelines.

Applications of Quantitative Systems Pharmacology in Model-Informed Drug Discovery: Perspective on Impact and Opportunities

Quantitative systems pharmacology (QSP) approaches have been increasingly applied in the pharmaceutical since the landmark white paper published in 2011 by a National Institutes of Health working group brought attention to the discipline. In this perspective, we discuss QSP in the context of other modeling approaches and highlight the impact of QSP across various stages of drug development and therapeutic areas. We discuss challenges to the field as well as future opportunities.

Quantitative Systems Pharmacology: An Exemplar Model-Building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development

Quantitative systems pharmacology (QSP), a mechanistically oriented form of drug and disease modeling, seeks to address a diverse set of problems in the discovery and development of therapies. These problems bring a considerable amount of variability and uncertainty inherent in the nonclinical and clinical data. Likewise, the available modeling techniques and related software tools are manifold. Appropriately, the development, qualification, application, and impact of QSP models have been similarly varied. In this review, we describe the progressive maturation of a QSP modeling workflow: a necessary step for the efficient, reproducible development and qualification of QSP models, which themselves are highly iterative and evolutive. Furthermore, we describe three applications of QSP to impact drug development; one supporting new indications for an approved antidiabetic clinical asset through mechanistic hypothesis generation, one highlighting efficacy and safety differentiation within the sodium‐glucose cotransporter‐2 inhibitor drug class, and one enabling rational selection of immuno‐oncology drug combinations.

Molecular Modeling Approaches for the Prediction of Selected Pharmacokinetic Properties

Poor profiles of potential drug candidates, including pharmacokinetic properties, have been acknowledged as a significant hindrance to the development of modern therapeutics. Contemporary drug discovery and development would be incomplete without the aid of molecular modeling (in-silico) techniques, allowing the prediction of pharmacokinetic properties such as clearance, unbound fraction, volume of distribution and bioavailability. As with all models, in-silico approaches are subject to their interpretability, a trait that must be balanced with accuracy when considering the development of new methods. The best models will always require reliable data to inform them, presenting significant challenges, particularly when appropriate in-vitro or in-vivo data may be difficult or time-consuming to obtain. This article seeks to review some of the key in-silico techniques used to predict key pharmacokinetic properties and give commentary on the current and future directions of the field.

Model-Informed Drug Development for Ixazomib, an Oral Proteasome Inhibitor

Model-informed drug development (MIDD) was central to the development of the oral proteasome inhibitor ixazomib, facilitating internal decisions (switch from body surface area (BSA)-based to fixed dosing, inclusive phase III trials, portfolio prioritization of ixazomib-based combinations, phase III dose for maintenance treatment), regulatory review (model-informed QT analysis, benefit-risk of 4 mg dose), and product labeling (absolute bioavailability and intrinsic/extrinsic factors). This review discusses the impact of MIDD in enabling patient-centric therapeutic optimization during the development of ixazomib.

Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology

Two technologies that have emerged in the last decade offer a new paradigm for modern pharmacology, as well as drug discovery and development. Quantitative systems pharmacology (QSP) is a complementary approach to traditional, target-centric pharmacology and drug discovery and is based on an iterative application of computational and systems biology methods with multiscale experimental methods, both of which include models of ADME-Tox and disease. QSP has emerged as a new approach due to the low efficiency of success in developing therapeutics based on the existing target-centric paradigm. Likewise, human microphysiology systems (MPS) are experimental models complementary to existing animal models and are based on the use of human primary cells, adult stem cells, and/or induced pluripotent stem cells (iPSCs) to mimic human tissues and organ functions/structures involved in disease and ADME-Tox. Human MPS experimental models have been developed to address the relatively low concordance of human disease and ADME-Tox with engineered, experimental animal models of disease. The integration of the QSP paradigm with the use of human MPS has the potential to enhance the process of drug discovery and development.

A Survey of Software Tool Utilization and Capabilities for Quantitative Systems Pharmacology: What We Have and What We Need

Quantitative systems pharmacology (QSP) is a rapidly emerging discipline with application across a spectrum of challenges facing the pharmaceutical industry, including mechanistically informed prioritization of target pathways and combinations in discovery, target population, and dose expansion decisions early in clinical development, and analyses for regulatory authorities late in clinical development. QSP's development has influences from physiologic modeling, systems biology, physiologically‐based pharmacokinetic modeling, and pharmacometrics. Given a varied scientific heritage, a variety of tools to accomplish the demands of model development, application, and model‐based analysis of available data have been developed. We report the outcome from a community survey and resulting analysis of how modelers view the impact and growth of QSP, how they utilize existing tools, and capabilities they need improved to further accelerate their impact on drug development. These results serve as a benchmark and roadmap for advancements to the QSP tool set.

Use of a Systems Pharmacology Model Based Approach Toward Dose Optimization of Parathyroid Hormone Therapy in Hypoparathyroidism

We present an application of a quantitative systems pharmacology (QSP) model to support a regulatory decision, specifically in assessing the adequacy of the proposed dosing regimen. On January 23, 2015, the US Food and Drug Administration (FDA) approved Natpara (human parathyroid hormone (PTH)) to control hypocalcemia in patients with hypoparathyroidism. Clinical trial results indicated that although once-daily PTH reduced calcium and vitamin D dose requirement while maintaining the normocalcemia, the regimen was not adequate to control hypercalciuria. We hypothesized that the lack of control on urinary calcium excretion was due to the short half-life of PTH. The QSP model-based simulations indicated that a more frequent dosing regimen may provide better control on hypercalciuria while maintaining normocalcemia. A postmarketing trial was recommended to assess pharmacokinetics (PKs) and pharmacodynamics (PDs) of PTH dose and dosing regimen. Although other modeling approaches may be feasible, in this specific case, QSP model-based simulations fulfilled the information gap to support recommendations of this postmarketing trial.

Harvesting the promise of AOPs: An assessment and recommendations

The Adverse Outcome Pathway (AOP) concept is a knowledge assembly and communication tool to facilitate the transparent translation of mechanistic information into outcomes meaningful to the regulatory assessment of chemicals. The AOP framework and associated knowledgebases (KBs) have received significant attention and use in the regulatory toxicology community. However, it is increasingly apparent that the potential stakeholder community for the AOP concept and AOP KBs is broader than scientists and regulators directly involved in chemical safety assessment. In this paper we identify and describe those stakeholders who currently-or in the future-could benefit from the application of the AOP framework and knowledge to specific problems. We also summarize the challenges faced in implementing pathway-based approaches such as the AOP framework in biological sciences, and provide a series of recommendations to meet critical needs to ensure further progression of the framework as a useful, sustainable and dependable tool supporting assessments of both human health and the environment. Although the AOP concept has the potential to significantly impact the organization and interpretation of biological information in a variety of disciplines/applications, this promise can only be fully realized through the active engagement of, and input from multiple stakeholders, requiring multi-pronged substantive long-term planning and strategies.

Maximizing the impact of microphysiological systems with in vitro-in vivo translation

Microphysiological systems (MPS) hold promise for improving therapeutic drug approval rates by providing more physiological, human-based, in vitro assays for preclinical drug development activities compared to traditional in vitro and animal models. Here, we first summarize why MPSs are needed in pharmaceutical development, and examine how MPS technologies can be utilized to improve preclinical efforts. We then provide the perspective that the full impact of MPS technologies will be realized only when robust approaches for in vitro-in vivo (MPS-to-human) translation are developed and utilized, and explain how the burgeoning field of quantitative systems pharmacology (QSP) can fill that need.

Preclinical QSP Modeling in the Pharmaceutical Industry: An IQ Consortium Survey Examining the Current Landscape

A cross-industry survey was conducted to assess the landscape of preclinical quantitative systems pharmacology (QSP) modeling within pharmaceutical companies. This article presents the survey results, which provide insights on the current state of preclinical QSP modeling in addition to future opportunities. Our results call attention to the need for an aligned definition and consistent terminology around QSP, yet highlight the broad applicability and benefits preclinical QSP modeling is currently delivering.

Big Data in Drug Discovery

Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.

Innovative organotypic in vitro models for safety assessment: aligning with regulatory requirements and understanding models of the heart, skin, and liver as paradigms

The development of improved, innovative models for the detection of toxicity of drugs, chemicals, or chemicals in cosmetics is crucial to efficiently bring new products safely to market in a cost-effective and timely manner. In addition, improvement in models to detect toxicity may reduce the incidence of unexpected post-marketing toxicity and reduce or eliminate the need for animal testing. The safety of novel products of the pharmaceutical, chemical, or cosmetics industry must be assured; therefore, toxicological properties need to be assessed. Accepted methods for gathering the information required by law for approval of substances are often animal methods. To reduce, refine, and replace animal testing, innovative organotypic in vitro models have emerged. Such models appear at different levels of complexity ranging from simpler, self-organized three-dimensional (3D) cell cultures up to more advanced scaffold-based co-cultures consisting of multiple cell types. This review provides an overview of recent developments in the field of toxicity testing with in vitro models for three major organ types: heart, skin, and liver. This review also examines regulatory aspects of such models in Europe and the UK, and summarizes best practices to facilitate the acceptance and appropriate use of advanced in vitro models.

Leveraging model-informed approaches for drug discovery and development in the cardiovascular space

Cardiovascular disease remains a significant global health burden, and development of cardiovascular drugs in the current regulatory environment often demands large and expensive cardiovascular outcome trials. Thus, the use of quantitative pharmacometric approaches which can help enable early Go/No Go decision making, ensure appropriate dose selection, and increase the likelihood of successful clinical trials, have become increasingly important to help reduce the risk of failed cardiovascular outcomes studies. In addition, cardiovascular safety is an important consideration for many drug development programs, whether or not the drug is designed to treat cardiovascular disease; modeling and simulation approaches also have utility in assessing risk in this area. Herein, examples of modeling and simulation applied at various stages of drug development, spanning from the discovery stage through late-stage clinical development, for cardiovascular programs are presented. Examples of how modeling approaches have been utilized in early development programs across various therapeutic areas to help inform strategies to mitigate the risk of cardiovascular-related adverse events, such as QTc prolongation and changes in blood pressure, are also presented. These examples demonstrate how more informed drug development decisions can be enabled by modeling and simulation approaches in the cardiovascular area.

Crowdsourced Asparagus Urinary Odor Population Kinetics

The consumption of asparagus is associated with the production of malodorous urine with considerable interindividual variability (IIV). To characterize the urinary odor kinetics after consumption of asparagus spears, we conducted a study with consenting attendees from two American Society for Clinical Pharmacology and Therapeutics (ASCPT) meetings. Subjects were randomized to eat a specific number of asparagus spears, and then asked to report their urinary odor perception. Eighty‐seven subjects were included in the final analysis. A mixed effect proportional odds model was developed that adequately characterized the dose‐response relationship. We estimated the half‐life of the asparagus effect on malodorous urine to be 4.7 hours (relative standard error (RSE) = 13.2%), and identified a dose‐response slope term with good precision (24.3%). Age was found as the predictor of IIV in slope estimates. This study design and tools can be used as a demonstration “crowdsourcing” project for studying population kinetics in organizational and educational settings.

Is systems pharmacology ready to impact upon therapy development? A study on the cholesterol biosynthesis pathway

Background and Purpose

An ever‐growing wealth of information on current drugs and their pharmacological effects is available from online databases. As our understanding of systems biology increases, we have the opportunity to predict, model and quantify how drug combinations can be introduced that outperform conventional single‐drug therapies. Here, we explore the feasibility of such systems pharmacology approaches with an analysis of the mevalonate branch of the cholesterol biosynthesis pathway.

Experimental Approach

Using open online resources, we assembled a computational model of the mevalonate pathway and compiled a set of inhibitors directed against targets in this pathway. We used computational optimization to identify combination and dose options that show not only maximal efficacy of inhibition on the cholesterol producing branch but also minimal impact on the geranylation branch, known to mediate the side effects of pharmaceutical treatment.

Key Results

We describe serious impediments to systems pharmacology studies arising from limitations in the data, incomplete coverage and inconsistent reporting. By curating a more complete dataset, we demonstrate the utility of computational optimization for identifying multi‐drug treatments with high efficacy and minimal off‐target effects.

Conclusion and Implications

We suggest solutions that facilitate systems pharmacology studies, based on the introduction of standards for data capture that increase the power of experimental data. We propose a systems pharmacology workflow for the refinement of data and the generation of future therapeutic hypotheses.

Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy

Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug-target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure-drug-target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein, and kaempferol) with high scores were validated by various literature studies. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug-target interactions of natural products with known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram, and metformin) with new mechanism-of-action were validated by various published experimental evidence. In summary, this study offers powerful computational systems pharmacology approaches and tools for the development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.

Humans Vary, So Cardiac Models Should Account for That Too!

The utilization of mathematical modeling and simulation in drug development encompasses multiple mathematical techniques and the location of a drug candidate in the development pipeline. Historically speaking they have been used to analyze experimental data (i.e., Hill equation) and clarify the involved physical and chemical processes (i.e., Fick laws and drug molecule diffusion). In recent years the advanced utilization of mathematical modeling has been an important part of the regulatory review process. Physiologically based pharmacokinetic (PBPK) models identify the need to conduct specific clinical studies, suggest specific study designs and propose appropriate labeling language. Their application allows the evaluation of the influence of intrinsic (e.g., age, gender, genetics, disease) and extrinsic [e.g., dosing schedule, drug-drug interactions (DDIs)] factors, alone or in combinations, on drug exposure and therefore provides accurate population assessment. A similar pathway has been taken for the assessment of drug safety with cardiac safety being one the most advanced examples. Mechanistic mathematical model-informed safety evaluation, with a focus on drug potential for causing arrhythmias, is now discussed as an element of the Comprehensive in vitro Proarrhythmia Assay. One of the pillars of this paradigm is the use of an in silico model of the adult human ventricular cardiomyocyte to integrate in vitro measured data. Existing examples (in vitro—in vivo extrapolation with the use of PBPK models) suggest that deterministic, epidemiological and clinical data based variability models can be merged with the mechanistic models describing human physiology. There are other methods available, based on the stochastic approach and on population of models generated by randomly assigning specific parameter values (ionic current conductance and kinetic) and further pruning. Both approaches are briefly characterized in this manuscript, in parallel with the drug-specific variability.

QSP Toolbox: Computational Implementation of Integrated Workflow Components for Deploying Multi-Scale Mechanistic Models

Quantitative systems pharmacology (QSP) modeling has become increasingly important in pharmaceutical research and development, and is a powerful tool to gain mechanistic insights into the complex dynamics of biological systems in response to drug treatment. However, even once a suitable mathematical framework to describe the pathophysiology and mechanisms of interest is established, final model calibration and the exploration of variability can be challenging and time consuming. QSP models are often formulated as multi-scale, multi-compartment nonlinear systems of ordinary differential equations. Commonly accepted modeling strategies, workflows, and tools have promise to greatly improve the efficiency of QSP methods and improve productivity. In this paper, we present the QSP Toolbox, a set of functions, structure array conventions, and class definitions that computationally implement critical elements of QSP workflows including data integration, model calibration, and variability exploration. We present the application of the toolbox to an ordinary differential equations-based model for antibody drug conjugates. As opposed to a single stepwise reference model calibration, the toolbox also facilitates simultaneous parameter optimization and variation across multiple in vitro, in vivo, and clinical assays to more comprehensively generate alternate mechanistic hypotheses that are in quantitative agreement with available data. The toolbox also includes scripts for developing and applying virtual populations to mechanistic exploration of biomarkers and efficacy. We anticipate that the QSP Toolbox will be a useful resource that will facilitate implementation, evaluation, and sharing of new methodologies in a common framework that will greatly benefit the community.

Translational learning from clinical studies predicts drug pharmacokinetics across patient populations

Early indication of late-stage failure of novel candidate drugs could be facilitated by continuous integration, assessment, and transfer of knowledge acquired along pharmaceutical development programs. We here present a translational systems pharmacology workflow that combines drug cocktail probing in a specifically designed clinical study, physiologically based pharmacokinetic modeling, and Bayesian statistics to identify and transfer (patho-)physiological and drug-specific knowledge across distinct patient populations. Our work builds on two clinical investigations, one with 103 healthy volunteers and one with 79 diseased patients from which we systematically derived physiological information from pharmacokinetic data for a reference probe drug (midazolam) at the single-patient level. Taking into account the acquired knowledge describing (patho-)physiological alterations in the patient cohort allowed the successful prediction of the population pharmacokinetics of a second, candidate probe drug (torsemide) in the patient population. In addition, we identified significant relations of the acquired physiological processes to patient metadata from liver biopsies. The presented prototypical systems pharmacology approach is a proof of concept for model-based translation across different stages of pharmaceutical development programs. Applied consistently, it has the potential to systematically improve predictivity of pharmacokinetic simulations by incorporating the results of clinical trials and translating them to subsequent studies.

Physiologically based modeling together with Bayesian statistics allows the prediction of drug pharmacokinetics in specific patient populations. An interdisciplinary group of clinicians and computational scientists led by Dr. Lars Kuepfer from Bayer developed a generic workflow consisting of several consecutive learning steps where knowledge about both individual physiology as well as drug physicochemistry can be efficiently derived from plasma concentration profiles. The acquired information is then be used for the prediction of the pharmacokinetic behavior of a new drug candidate in a diseased population. This allows to simulate the variability in drug exposure virtually before starting clinical investigation in real patients in order to evaluate drug safety or efficacy through the simulation of virtual populations. Further development of this workflow could improve the safety of clinical development programs to assess the risk-benefit ratio of novel drug candidates in silico.

An In Silico Knockout Model for Gastrointestinal Absorption Using a Systems Pharmacology Approach - Development and Application for Ketones

Gastrointestinal absorption and disposition of ketones is complex. Recent work describing the pharmacokinetics (PK) of d-β-hydroxybutyrate (BHB) following oral ingestion of a ketone monoester ((R)-3-hydroxybutyl (R)-3-hydroxybutyrate) found multiple input sites, nonlinear disposition and feedback on endogenous production. In the current work, a human systems pharmacology model for gastrointestinal absorption and subsequent disposition of small molecules (monocarboxylic acids with molecular weight < 200 Da) was developed with an application to a ketone monoester. The systems model was developed by collating the information from the literature and knowledge gained from empirical population modelling of the clinical data. In silico knockout variants of this systems model were used to explore the mechanism of gastrointestinal absorption of ketones. The knockouts included active absorption across different regions in the gut and also a passive diffusion knockout, giving 10 gut knockouts in total. Exploration of knockout variants has suggested that there are at least three distinct regions in the gut that contribute to absorption of ketones. Passive diffusion predominates in the proximal gut and active processes contribute to the absorption of ketones in the distal gut. Low doses are predominantly absorbed from the proximal gut by passive diffusion whereas high doses are absorbed across all sites in the gut. This work has provided mechanistic insight into the absorption process of ketones, in the form of unique in silico knockouts that have potential for application with other therapeutics. Future studies on absorption process of ketones are suggested to substantiate findings in this study.

The promises of quantitative systems pharmacology modelling for drug development

Recent growth in annual new therapeutic entity (NTE) approvals by the U.S. Food and Drug Administration (FDA) suggests a positive trend in current research and development (R&D) output. Prior to this, the cost of each NTE was considered to be rising exponentially, with compound failure occurring mainly in clinical phases. Quantitative systems pharmacology (QSP) modelling, as an additional tool in the drug discovery arsenal, aims to further reduce NTE costs and improve drug development success. Through in silico mathematical modelling, QSP can simulate drug activity as perturbations in biological systems and thus understand the fundamental interactions which drive disease pathology, compound pharmacology and patient response. Here we review QSP, pharmacometrics and systems biology models with respect to the diseases covered as well as their clinical relevance and applications. Overall, the majority of modelling focus was aligned with the priority of drug-discovery and clinical trials. However, a few clinically important disease categories, such as Immune System Diseases and Respiratory Tract Diseases, were poorly covered by computational models. This suggests a possible disconnect between clinical and modelling agendas. As a standard element of the drug discovery pipeline the uptake of QSP might help to increase the efficiency of drug development across all therapeutic indications.

Disease Progression Modeling: Key Concepts and Recent Developments

Disease modeling involves the use of mathematical functions to describe quantitatively the time course of disease progression. In order to characterize the natural progression of disease, these models generally incorporate longitudinal data for some biomarker(s) of disease severity or can incorporate more direct measures of disease severity. Disease models are also often linked to pharmacokinetic-pharmacodynamic models so that the influence of drug treatment on disease progression can be quantified and evaluated. Regulatory agencies have embraced disease progression models as powerful tools that can be used to improve drug development productivity. This article provides a brief overview of key concepts in disease progression modeling followed by illustrative examples from models for Alzheimer's disease. Finally, recent novel applications in which disease progression models have been linked to cost-effectiveness analysis and genomic analysis are described.

Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success

Hierarchical processes spanning several orders of magnitude of both space and time underlie nearly all cancers. Multi-scale statistical, mathematical, and computational modeling methods are central to designing, implementing and assessing treatment strategies that account for these hierarchies. The basic science underlying these modeling efforts is maturing into a new discipline that is close to influencing and facilitating clinical successes. The purpose of this review is to capture the state-of-the-art as well as the key barriers to success for multi-scale modeling in clinical oncology. We begin with a summary of the long-envisioned promise of multi-scale modeling in clinical oncology, including the synthesis of disparate data types into models that reveal underlying mechanisms and allow for experimental testing of hypotheses. We then evaluate the mathematical techniques employed most widely and present several examples illustrating their application as well as the current gap between pre-clinical and clinical applications. We conclude with a discussion of what we view to be the key challenges and opportunities for multi-scale modeling in clinical oncology.

A Perspective on Implementing a Quantitative Systems Pharmacology Platform for Drug Discovery and the Advancement of Personalized Medicine

Drug candidates exhibiting well-defined pharmacokinetic and pharmacodynamic profiles that are otherwise safe often fail to demonstrate proof-of-concept in phase II and III trials. Innovation in drug discovery and development has been identified as a critical need for improving the efficiency of drug discovery, especially through collaborations between academia, government agencies, and industry. To address the innovation challenge, we describe a comprehensive, unbiased, integrated, and iterative quantitative systems pharmacology (QSP)-driven drug discovery and development strategy and platform that we have implemented at the University of Pittsburgh Drug Discovery Institute. Intrinsic to QSP is its integrated use of multiscale experimental and computational methods to identify mechanisms of disease progression and to test predicted therapeutic strategies likely to achieve clinical validation for appropriate subpopulations of patients. The QSP platform can address biological heterogeneity and anticipate the evolution of resistance mechanisms, which are major challenges for drug development. The implementation of this platform is dedicated to gaining an understanding of mechanism(s) of disease progression to enable the identification of novel therapeutic strategies as well as repurposing drugs. The QSP platform will help promote the paradigm shift from reactive population-based medicine to proactive personalized medicine by focusing on the patient as the starting and the end point.

Systems pharmacology to predict drug safety in drug development

Ensuring that drugs are safe and effective is a very high priority for drug development and the US Food and Drug Administration review process. This is especially true today because of faster approval times and smaller clinical trials, especially in oncology and rare diseases. In light of these trends, systems pharmacology is seen as an essential strategy to understand and predict adverse drug events during drug development by analyzing interactions between drugs and multiple targets rather than the traditional "one-drug-one-target" approach. This commentary offers an overview of the current trends and challenges of using systems pharmacology to reduce the risks of unintended adverse events.

A Six-Stage Workflow for Robust Application of Systems Pharmacology

Quantitative and systems pharmacology (QSP) is increasingly being applied in pharmaceutical research and development. One factor critical to the ultimate success of QSP is the establishment of commonly accepted language, technical criteria, and workflows. We propose an integrated workflow that bridges conceptual objectives with underlying technical detail to support the execution, communication, and evaluation of QSP projects.