Systems pharmacology for drug discovery and development: paradigm shift or flash in the pan?

Quantification of the effect of GLP-1R agonists on body weight using in vitro efficacy information: An extension of the Hall body composition model

Obesity has become a major public health concern worldwide. Pharmacological interventions with the glucagon-like peptide-1 receptor agonists (GLP-1RAs) have shown promising results in facilitating weight loss and improving metabolic outcomes in individuals with obesity. Quantifying drug effects of GLP-1RAs on energy intake (EI) and body weight (BW) using a QSP modeling approach can further increase the mechanistic understanding of these effects, and support obesity drug development. An extensive literature-based dataset was created, including data from several diet, liraglutide and semaglutide studies and their effects on BW and related parameters. The Hall body composition model was used to quantify and predict effects on EI. The model was extended with (1) a lifestyle change/placebo effect on EI, (2) a weight loss effect on activity for the studies that included weight management support, and (3) a GLP-1R agonistic effect using in vitro potency efficacy information. The estimated reduction in EI of clinically relevant dosages of semaglutide (2.4 mg) and liraglutide (3.0 mg) was 34.5% and 13.0%, respectively. The model adequately described the resulting change in BW over time. At 20 weeks the change in BW was estimated to be -17% for 2.4 mg semaglutide and -8% for 3 mg liraglutide, respectively. External validation showed the model was able to predict the effect of semaglutide on BW in the STEP 1 study. The GLP-1RA body composition model can be used to quantify and predict the effect of novel GLP-1R agonists on BW and changes in underlying processes using early in vitro efficacy information.

An industry perspective on current QSP trends in drug development

2023 marks the 10th anniversary of Natpara's submission to the US FDA, which led to the first recorded regulatory interaction where a decision was supported by Quantitative and Systems Pharmacology (QSP) simulations. It had taken about 5 years for the timid QSP discipline to emerge as an effective Model-Informed Drug Development (MIDD) tool with visible impact in the pharmaceutical industry. Since then, the presence of QSP in the regulatory environment has continued to increase, to the point that the Agency reported 60 QSP submissions in 2020 alone, representing ~ 4% of their annual IND submissions [1]. What sort of industry mindset has enabled QSP to reach this level of success? How does QSP fit within the MIDD paradigm? Does QSP mean the same to Discovery and to Clinical Development projects? How do 'platforms' compare to 'fit-for-purpose' QSP models in an industrial setting? Can QSP and empirical Pharmacokinetic-Pharmacodynamic (PKPD) modelling be complementary? What level of validation is required to inform drug development decisions? This article reflects on all these questions, in particular addressing those audiences with limited line-of-sight into the drug industry decision-making machinery.

Emulation of Quantitative Systems Pharmacology models to accelerate virtual population inference in immuno-oncology

Quantitative Systems Pharmacology (QSP) models are increasingly being applied for target discovery and dose selection in immuno-oncology (IO). Typical application involves virtual trial, a simulation of a virtual population of hundreds of model instances with model inputs reflecting individual variability. While the structure of the model and initial parameterisation are based on literature describing the underlying biology, calibration of the virtual population by existing clinical data is frequently required to create tumour and patient population specific model instances. Since comparison of a virtual trial with clinical output requires hundreds of large-scale, non-linear model evaluations, the inference of a virtual population is computationally expensive, frequently becoming a bottleneck. Here, we present novel approach to virtual population inference in IO using emulation of the QSP model and an objective function based on Kolmogorov-Smirnov statistics to maximise congruence of simulated and observed clinical tumour size distributions. We sample the parameter space of a QSP IO model to collect a set of tumour growth time profiles. We evaluate performance of several machine learning approaches in interpolating these time profiles and create a surrogate model, which computes tumor growth profiles faster than the original model and allows examination of tens of millions of virtual patients. We use the surrogate model to infer a virtual population maximising congruence with the waterfall plot of a pembrolizumab clinical trial. We believe that our approach is applicable not only in QSP IO, but also in other applications where virtual populations need to be inferred for computationally expensive mechanistic models.

Decatastrophizing research irreproducibility

The reported inability to replicate research findings from the published literature precipitated extensive efforts to identify and correct perceived deficiencies in the execution and reporting of biomedical research. Despite these efforts, quantification of the magnitude of irreproducible research or the effectiveness of associated remediation initiatives, across diverse biomedical disciplines, has made little progress over the last decade. The idea that science is self-correcting has been further challenged in recent years by the proliferation of unverified or fraudulent scientific content generated by predatory journals, paper mills, pre-print server postings, and the inappropriate use of artificial intelligence technologies. The degree to which the field of pharmacology has been negatively impacted by these evolving pressures is unknown. Regardless of these ambiguities, pharmacology societies and their associated journals have championed best practices to enhance the experimental rigor and reporting of pharmacological research. The value of transparent and independent validation of raw data generation and its analysis in basic and clinical research is exemplified by the discovery, development, and approval of Highly Effective Modulator Therapy (HEMT) for Cystic Fibrosis (CF) patients. This provides a didactic counterpoint to concerns regarding the current state of biomedical research. Key features of this important therapeutic advance include objective construction of basic and translational research hypotheses, associated experimental designs, and validation of experimental effect sizes with quantitative alignment to meaningful clinical endpoints with input from the FDA, which enhanced scientific rigor and transparency with real world deliverables for patients in need.

QSP Designer: Quantitative systems pharmacology modeling with modular biological process map notation and multiple language code generation

Typical Quantitative Systems Pharmacology (QSP) workflows involve discussion of biology, supported by graphical diagrams, followed by construction of large Ordinary Differential Equation models. QSP Designer facilitates this process by providing enhanced graphical notation, which enables hierarchical presentation with modules and handling of combinatorial complexity with diagram node arrays. Whereas the software includes a simulation engine, a major feature is full model code generation in MATLAB, R, C, and Julia to support multiple modeling communities.

Empowering drug development: Leveraging insights from imaging technologies to enable the advancement of digital health technologies

The US Food and Drug Administration (FDA) has publicly recognized the importance of improving drug development efficiency, deeming translational biomarkers a top priority. The use of imaging biomarkers has been associated with increased rates of drug approvals. An appropriate level of validation provides a pragmatic way to choose and implement these biomarkers. Standardizing imaging modality selection, data acquisition protocols, and image analysis (in ways that are agnostic to equipment and algorithms) have been key to imaging biomarker deployment. The best known examples come from studies done via precompetitive collaboration efforts, which enable input from multiple stakeholders and data sharing. Digital health technologies (DHTs) provide an opportunity to measure meaningful aspects of patient health, including patient function, for extended periods of time outside of the hospital walls, with objective, sensor-based measures. We identified the areas where learnings from the imaging biomarker field can accelerate the adoption and widespread use of DHTs to develop novel treatments. As with imaging, technical validation parameters and performance acceptance thresholds need to be established. Approaches amenable to multiple hardware options and data processing algorithms can be enabled by sharing DHT data and by cross-validating algorithms. Data standardization and creation of shared databases will be vital. Pre-competitive consortia (public-private partnerships and professional societies that bring together all stakeholders, including patient organizations, industry, academic experts, and regulators) will advance the regulatory maturity of DHTs in clinical trials.

Quality Assurance of PBPK Modeling Platforms and Guidance on Building, Evaluating, Verifying and Applying PBPK Models Prudently under the Umbrella of Qualification: Why, When, What, How and By Whom?

Modeling and simulation emerges as a fundamental asset of drug development. Mechanistic modeling builds upon its strength to integrate various data to represent a detailed structural knowledge of a physiological and biological system and is capable of informing numerous drug development and regulatory decisions via extrapolations outside clinically studied scenarios. Herein, physiologically based pharmacokinetic (PBPK) modeling is the fastest growing branch, and its use for particular applications is already expected or explicitly recommended by regulatory agencies. Therefore, appropriate applications of PBPK necessitates trust in the predictive capability of the tool, the underlying software platform, and related models. That has triggered a discussion on concepts of ensuring credibility of model-based derived conclusions. Questions like 'why', 'when', 'what', 'how' and 'by whom' remain open. We seek for harmonization of recent ideas, perceptions, and related terminology. First, we provide an overview on quality assurance of PBPK platforms with the two following concepts. Platform validation: ensuring software integrity, security, traceability, correctness of mathematical models and accuracy of algorithms. Platform qualification: demonstrating the predictive capability of a PBPK platform within a particular context of use. Second, we provide guidance on executing dedicated PBPK studies. A step-by-step framework focuses on the definition of the question of interest, the context of use, the assessment of impact and risk, the definition of the modeling strategy, the evaluation of the platform, performing model development including model building, evaluation and verification, the evaluation of applicability to address the question, and the model application under the umbrella of a qualified platform.

Mechanistic Models of COVID-19: Insights into Disease Progression, Vaccines, and Therapeutics

The COVID-19 pandemic has severely impacted health systems and economies worldwide. Significant global efforts are therefore ongoing to improve vaccine efficacies, optimize vaccine deployment, and develop new antiviral therapies to combat the pandemic. Mechanistic viral dynamics and quantitative systems pharmacology models of SARS-CoV-2 infection, vaccines, immunomodulatory agents, and antiviral therapeutics have played a key role in advancing our understanding of SARS-CoV-2 pathogenesis and transmission, the interplay between innate and adaptive immunity to influence the outcomes of infection, effectiveness of treatments, mechanisms and performance of COVID-19 vaccines, and the impact of emerging SARS-CoV-2 variants. Here, we review some of the critical insights provided by these models and discuss the challenges ahead.

Translating pharmacology models effectively to predict therapeutic benefit

Many in vitro and in vivo models are used in pharmacological research to evaluate the role of targeted proteins in a disease. Understanding the translational relevance and limitation of these models for analyzing the disposition, pharmacokinetic/pharmacodynamic (PK/PD) profile, mechanism, and efficacy of a drug, is essential when selecting the most appropriate model of the disease of interest and predicting clinically efficacious doses of the investigational drug. Here, we review selected animal models used in ophthalmology, infectious diseases, oncology, autoimmune diseases, and neuroscience. Each area has specific challenges around translatability and determination of an efficacious dose: new patient-specific dosing methods could help overcome these limitations.

Mechanistic modelling of enzyme-restoration effects for new recombinant liver-targeted proteins in acute intermittent porphyria

Background and Purpose

Acute intermittent porphyria (AIP) is a rare disease caused by a genetic mutation in the hepatic activity of the porphobilinogen‐deaminase. We aimed to develop a mechanistic model of the enzymatic restoration effects of a novel therapy based on the administration of different formulations of recombinant human‐PBGD (rhPBGD) linked to the ApoAI lipoprotein. This fusion protein circulates in blood, incorporating into HDL and penetrating hepatocytes.

Experimental Approach

Single i.v. dose of different formulations of rhPBGD linked to ApoAI were administered to AIP mice in which a porphyric attack was triggered by i.p. phenobarbital. Data consist on 24 h urine excreted amounts of heme precursors, 5‐aminolevulinic acid (ALA), PBG and total porphyrins that were analysed using non‐linear mixed‐effects analysis.

Key Results

The mechanistic model successfully characterized over time the amounts excreted in urine of the three heme precursors for different formulations of rhPBGD and unravelled several mechanisms in the heme pathway, such as the regulation in ALA synthesis by heme. Treatment with rhPBGD formulations restored PBGD activity, increasing up to 51 times the value of the rate of tPOR formation estimated from baseline. Model‐based simulations showed that several formulation prototypes provided efficient protective effects when administered up to 1 week prior to the occurrence of the AIP attack.

Conclusion and Implications

The model developed had excellent performance over a range of doses and formulation type. This mechanistic model warrants use beyond ApoAI‐conjugates and represents a useful tool towards more efficient drug treatments of other enzymopenias as well as for acute intermittent porphyria.

Virtual clinical trial simulations for a novel KRASG12C inhibitor (ASP2453) in non-small cell lung cancer

KRAS is a small GTPase family protein that relays extracellular growth signals to cell nucleus. KRAS^G12C mutations lead to constitutive proliferation signaling and are prevalent across human cancers. ASP2453 is a novel, highly potent, and selective inhibitor of KRAS^G12C . Although preclinical data suggested impressive efficacy, it remains unclear whether ASP2453 will show more favorable clinical response compared to more advanced competitors, such as AMG 510. Here, we developed a quantitative systems pharmacology (QSP) model linking KRAS signaling to tumor growth in patients with non-small cell lung cancer. The model was parameterized using in vitro ERK1/2 phosphorylation and in vivo xenograft data for ASP2453. Publicly disclosed clinical data for AMG 510 were used to generate a virtual population, and tumor size changes in response to ASP2453 and AMG 510 were simulated. The QSP model predicted ASP2453 exhibits greater clinical response than AMG 510, supporting potential differentiation and critical thinking for clinical trials.

In silico identification of natural products from Traditional Chinese Medicine for cancer immunotherapy

Advances in immunotherapy have revolutionized treatments in many types of cancer. Traditional Chinese Medicine (TCM), which has a long history of clinical adjuvant application against cancer, is emerging as an important medical resource for developing innovative cancer treatments, including immunotherapy. In this study, we developed a quantitative and systems pharmacology-based framework to identify TCM-derived natural products for cancer immunotherapy. Specifically, we integrated 381 cancer immune response-related genes and a compound-target interaction network connecting 3273 proteins and 766 natural products from 66 cancer-related herbs based on literature-mining. Via systems pharmacology-based prediction, we uncovered 182 TCM-derived natural products having potential anti-tumor immune responses effect. Importantly, 32 of the 49 most promising natural products (success rate = 65.31%) are validated by multiple evidence, including published experimental data from clinical studies, in vitro and in vivo assays. We further identified the mechanism-of-action of TCM in cancer immunotherapy using network-based functional enrichment analysis. We showcased that three typical natural products (baicalin, wogonin, and oroxylin A) in Huangqin (Scutellaria baicalensis Georgi) potentially overcome resistance of known oncology agents by regulating tumor immunosuppressive microenvironments. In summary, this study offers a novel and effective systems pharmacology infrastructure for potential cancer immunotherapeutic development by exploiting the medical wealth of natural products in TCM.

Systems pharmacology approach uncovers Ligustilide attenuates experimental colitis in mice by inhibiting PPARγ-mediated inflammation pathways

Inflammatory bowel disease (IBD) is a chronic idiopathic disorder causing inflammation in the gastro-intestinal tract, which is lack of effective drug targets and medications. To identify novel therapeutic agents against consistent targets, we exploited a systems pharmacology-driven framework that incorporates drug-target networks of natural product and IBD disease genes. Our in silico approach found that Ligustilide (LIG), one of the major active components of Angelica acutiloba and Cnidium Officinale, potently attenuated IBD. The following in vivo and in vitro results demonstrated that LIG prevented experimental mice colitis induced by dextran sulfate sodium (DSS) via suppressing inflammatory cell infiltration, the activity of MPO and iNOS, and the expression and production of IL-1β, IL-6, and TNF-α. Subsequently, the network analysis helped to validate that LIG alleviated colitis by inhibiting NF-κB and MAPK/AP-1 pathway through activating PPARγ, which were further confirmed in RAW 264.7 cells and bone marrow-derived macrophages in vitro. In summary, this study reveals that LIG activated PPARγ to inhibit the activation of NF-κB and AP-1 signaling thus eventually alleviated DSS-induced colitis, which has promising activities and may serve as a candidate for the treatment of IBD.Graphical abstract This study suggested novel computational and experimental pharmacology approaches to identify potential IBD therapeutic agents by exploiting polypharmacology of natural products. We demonstrated that LIG could attenuate inflammation in IBD by inhibiting NF-κB and AP-1 pathways via PPARγ activation to reduce the expression of pro-inflammatory cytokines in macrophages. These findings offer comprehensive pre-clinical evidence that LIG may serve as a promising candidate for IBD therapy in the future. Graphical headlights: 1. Systems pharmacology uncovered Ligustilide attenuates experimental colitis in mice. 2. Network-based analysis predicted the mechanism of Ligustilide against IBD, which was validated by inhibiting PPARγ-mediated inflammation pathways. 3. Ligustilide activated PPARγ to inhibit NF-κB and AP-1 activation thus eventually alleviated DSS-induced colitis.4. Ligustilide has promising activities and may serve as a candidate for the treatment of IBD.

Use of translational modeling and simulation for quantitative comparison of PF-06804103, a new generation HER2 ADC, with Trastuzumab-DM1

A modeling and simulation approach was used for quantitative comparison of a new generation HER2 antibody drug conjugate (ADC, PF-06804103) with trastuzumab-DM1 (T-DM1). To compare preclinical efficacy, the pharmacokinetic (PK)/pharmacodynamic (PD) relationship of PF-06804103 and T-DM1 was determined across a range of mouse tumor xenograft models, using a tumor growth inhibition model. The tumor static concentration was assigned as the minimal efficacious concentration. PF-06804103 was concluded to be more potent than T-DM1 across cell lines studied. TSCs ranged from 1.0 to 9.8 µg/mL (n = 7) for PF-06804103 and from 4.7 to 29 µg/mL (n = 5) for T-DM1. Two experimental models which were resistant to T-DM1, responded to PF-06804103 treatment. A mechanism-based target mediated drug disposition (TMDD) model was used to predict the human PK of PF-06804103. This model was constructed and validated based on T-DM1 which has non-linear PK at doses administered in the clinic, driven by binding to shed HER2. Non-linear PK is predicted for PF-06804103 in the clinic and is dependent upon circulating HER2 extracellular domain (ECD) concentrations. The models were translated to human and suggested greater efficacy for PF-06804103 compared to T-DM1. In conclusion, a fit-for-purpose translational PK/PD strategy for ADCs is presented and used to compare a new generation HER2 ADC with T-DM1.

Quantitative Systems Pharmacology Approaches for Immuno-Oncology: Adding Virtual Patients to the Development Paradigm

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno‐oncology (IO) the aim is to direct the patient’s own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD‐L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug‐development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds’ pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

Integration of Omics Data Sources to Inform Mechanistic Modeling of Immune-Oncology Therapies: A Tutorial for Clinical Pharmacologists

Application of contemporary molecular biology techniques to clinical samples in oncology resulted in the accumulation of unprecedented experimental data. These "omics" data are mined for discovery of therapeutic target combinations and diagnostic biomarkers. It is less appreciated that omics resources could also revolutionize development of the mechanistic models informing clinical pharmacology quantitative decisions about dose amount, timing, and sequence. We discuss the integration of omics data to inform mechanistic models supporting drug development in immuno-oncology. To illustrate our arguments, we present a minimal clinical model of the Cancer Immunity Cycle (CIC), calibrated for non-small cell lung carcinoma using tumor microenvironment composition inferred from transcriptomics of clinical samples. We review omics data resources, which can be integrated to parameterize mechanistic models of the CIC. We propose that virtual trial simulations with clinical Quantitative Systems Pharmacology platforms informed by omics data will be making increasing impact in the development of cancer immunotherapies.

Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.

Therapeutic potential of adenosine kinase inhibition-Revisited

Adenosine (ADO) is an endogenous protective regulator that restores cellular energy balance in response to tissue trauma. Extracellular ADO has a half-life of the order of seconds thus restricting its actions to tissues and cellular sites where it is released. Adenosine kinase (AK, ATP:adenosine 5'-phosphotransferase, EC 2.7.1.20) is a cytosolic enzyme that is the rate-limiting enzyme controlling extracellular ADO concentrations. Inhibition of AK can effectively increase ADO extracellular concentrations at tissue sites where pathophysiological changes occur. Highly potent and selective nucleoside and non-nucleoside AK inhibitors were discovered in the late 1990s that showed in vivo effects consistent with the augmentation of the actions of endogenous ADO in experimental models of pain, inflammation, and seizure activity. These data supported clinical development of several AK inhibitors for the management of epilepsy and chronic pain. However, early toxicological data demonstrated that nucleoside and non-nucleoside chemotypes produced hemorrhagic microfoci in brain in an apparent ADO receptor-dependent fashion. An initial oral report of these important toxicological findings was presented at an international conference but a detailed description of these data has not appeared in the peer-reviewed literature. In the two decades following the demise of these early AK-based clinical candidates, interest in AK inhibition has renewed based on preclinical data in the areas of renal protection, diabetic retinopathy, cardioprotection, and neurology. This review provides a summary of the pharmacology and toxicology data for several AK inhibitor chemotypes and the resulting translational issues associated with the development of AK inhibitors as viable therapeutic interventions.

Quantitative Systems Pharmacology Model of a Masked, Tumor-Activated Antibody

PROBODY therapeutics (Pb‐Tx) are protease‐activatable prodrugs of monoclonal antibodies (mAbs) designed to target tumors where protease activity is elevated while avoiding normal tissue. They are composed of a parental mAb, a mask that inhibits antibody binding to target, and a protease‐cleavable substrate between the mask and the mAb. We report a quantitative systems pharmacology model for the rational design and clinical translation of Pb‐Tx. The model adequately described monkey pharmacokinetic data following the administration of six anti‐CD166 Pb‐Tx of varying mask strength and substrate cleavability and captured the trend of decreasing Pb‐Tx systemic clearance with increasing mask strength. Projections to humans suggested both higher levels of Pb‐Tx in tumor relative to parental mAb and an optimal mask strength for maximizing tumor receptor–mediated uptake. Simulations further suggested the majority of circulating species in humans would be intact/masked Pb‐Tx, with no significant flux of cleaved/activated species from tumor to the systemic compartment.

Nonclinical immunogenicity risk assessment of therapeutic proteins

Therapeutic protein drugs have significantly improved the management of many severe and chronic diseases. However, their development and optimal clinical application are complicated by the induction of unwanted immune responses. Therapeutic protein-induced antidrug antibodies can alter drug pharmacokinetics and pharmacodynamics leading to impaired efficacy and occasionally serious safety issues. There has been a growing interest over the past decade in developing methods to assess the risk of unwanted immunogenicity during preclinical drug development, with the aim to mitigate the risk during the molecular design phase, clinical development and when products reach the market. Here, we discuss approaches to therapeutic protein immunogenicity risk assessment, with attention to assays and in vivo models used to mitigate this risk.

Cardiovascular disease models: A game changing paradigm in drug discovery and screening

Cardiovascular disease is the leading cause of death worldwide. Although investment in drug discovery and development has been sky-rocketing, the number of approved drugs has been declining. Cardiovascular toxicity due to therapeutic drug use claims the highest incidence and severity of adverse drug reactions in late-stage clinical development. Therefore, to address this issue, new, additional, replacement and combinatorial approaches are needed to fill the gap in effective drug discovery and screening. The motivation for developing accurate, predictive models is twofold: first, to study and discover new treatments for cardiac pathologies which are leading in worldwide morbidity and mortality rates; and second, to screen for adverse drug reactions on the heart, a primary risk in drug development. In addition to in vivo animal models, in vitro and in silico models have been recently proposed to mimic the physiological conditions of heart and vasculature. Here, we describe current in vitro, in vivo, and in silico platforms for modelling healthy and pathological cardiac tissues and their advantages and disadvantages for drug screening and discovery applications. We review the pathophysiology and the underlying pathways of different cardiac diseases, as well as the new tools being developed to facilitate their study. We finally suggest a roadmap for employing these non-animal platforms in assessing drug cardiotoxicity and safety.

Quantitative and systems pharmacology 4. Network-based analysis of drug pleiotropy on coronary artery disease

Despite recent advance of therapeutic development, coronary artery disease (CAD) remains one of the major issues to public health. The use of genomics and systems biology approaches to inform drug discovery and development have offered the possibilities for new target identification and in silico drug repurposing. In this study, we propose a network-based, systems pharmacology framework for target identification and drug repurposing in pharmacologic treatment and chemoprevention of CAD. Specifically, we build in silico models by integrating known drug-target interactions, CAD genes derived from the genetic and genomic studies, and the human protein-protein interactome. We demonstrate that the proposed in silico models can successfully uncover approved drugs and novel natural products in potentially treating and preventing CAD. In case studies, we highlight several approved drugs (e.g., fasudil, parecoxib, and dexamethasone) or natural products (e.g., resveratrol, luteolin, daidzein and caffeic acid) with new mechanism-of-action in chemical intervention of CAD by network analysis. In summary, this study offers a powerful systems pharmacology approach for target identification and in silico drug repurposing on CAD.

Understanding Hematological Toxicities Using Mathematical Modeling

Balancing antitumor efficacy with toxicity is a significant challenge, and drug-induced myelosuppression is a common dose-limiting toxicity of cancer treatments. Mathematical modeling has proven to be a powerful ally in this field, scaling results from animal models to humans, and designing optimized treatment regimens. Here we outline existing mathematical approaches for studying bone marrow toxicity, identify gaps in current understanding, and make future recommendations to advance this vital field of safety research further.

Why Drugs Fail in Late Stages of Development: Case Study Analyses from the Last Decade and Recommendations

New drug development is both resource and time intensive, where later clinical stages result in significant costs. We analyze recent late-stage failures to identify drugs where failures result from inadequate scientific advances as well as drugs where we believe pitfalls could have been avoided. These can be broadly classified into two categories: 1) where science is mature and the failures can be avoided through rigorous and prospectively determined decision-making criteria, scientific curiosity, and discipline to follow up on emerging findings; and 2) where problems encountered in Phase 3 failures cannot be explained at this time, as the science is not sufficiently advanced and companies/investigators need to recognize the possibility of deficiency of our knowledge. Through these case studies, key themes critical for successful drug development emerge-understanding the therapeutic pathway including receptor and signaling biology, pharmacological responses related to safety and efficacy, pharmacokinetics of the drug and exposure at target site, optimum dose, and dosing regimen; and identification of patient sub-populations likely to respond and will have a favorable benefit-risk profile, design of clinical trials, and a quantitative framework that can guide data-driven decision making. It is essential that the right studies are conducted early in the development process to answer the key questions, with the emphasis on learning in the early stages of development, whereas Phase 3 should be reserved for confirming the safety and efficacy. Utilization of innovative technology in identifying patients based on molecular signature of their disease, rapid assessment of pharmacological response, mechanistic modeling of emerging data, seamless operational processes to reduce start-up and wind-down time for clinical trials through use of electronic health records and data mining, and development of novel and objective clinical efficacy endpoints are some concepts for improving the success rate.

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.

Opportunities and pitfalls in clinical proof-of-concept: principles and examples

Clinical proof-of-concept trials crucially inform major resource deployment decisions. This paper discusses several mechanisms for enhancing their rigour and efficiency. The importance of careful consideration when using a surrogate endpoint is illustrated; situational effectiveness of run-in patient enrichment is explored; a versatile tool is introduced to ensure a strong pharmacological underpinning; the benefits of dose-titration are revealed by simulation; and the importance of adequately scheduled observations is shown. The general process of model-based trial design and analysis is described and several examples demonstrate the value in historical data, simulation-guided design, model-based analysis and trial adaptation informed by interim analysis.

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.

On the analysis of complex biological supply chains: From Process Systems Engineering to Quantitative Systems Pharmacology

The use of models in biology has become particularly relevant as it enables investigators to develop a mechanistic framework for understanding the operating principles of living systems as well as in quantitatively predicting their response to both pathological perturbations and pharmacological interventions. This application has resulted in a synergistic convergence of systems biology and pharmacokinetic-pharmacodynamic modeling techniques that has led to the emergence of quantitative systems pharmacology (QSP). In this review, we discuss how the foundational principles of chemical process systems engineering inform the progressive development of more physiologically-based systems biology models.

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 3. Network-Based Identification of New Targets for Natural Products Enables Potential Uses in Aging-Associated Disorders

Aging that refers the accumulation of genetic and physiology changes in cells and tissues over a lifetime has been shown a high risk of developing various complex diseases, such as neurodegenerative disease, cardiovascular disease and cancer. Over the past several decades, natural products have been demonstrated as anti-aging interveners via extending lifespan and preventing aging-associated disorders. In this study, we developed an integrated systems pharmacology infrastructure to uncover new indications for aging-associated disorders by natural products. Specifically, we incorporated 411 high-quality aging-associated human genes or human-orthologous genes from mus musculus (MM), saccharomyces cerevisiae (SC), caenorhabditis elegans (CE), and drosophila melanogaster (DM). We constructed a global drug-target network of natural products by integrating both experimental and computationally predicted drug-target interactions (DTI). We further built the statistical network models for identification of new anti-aging indications of natural products through integration of the curated aging-associated genes and drug-target network of natural products. High accuracy was achieved on the network models. We showcased several network-predicted anti-aging indications of four typical natural products (caffeic acid, metformin, myricetin, and resveratrol) with new mechanism-of-actions. In summary, this study offers a powerful systems pharmacology infrastructure to identify natural products for treatment of aging-associated disorders.

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.

Challenges in translational drug research in neuropathic and inflammatory pain: the prerequisites for a new paradigm

AIM

Despite an improved understanding of the molecular mechanisms of nociception, existing analgesic drugs remain limited in terms of efficacy in chronic conditions, such as neuropathic pain. Here, we explore the underlying pathophysiological mechanisms of neuropathic and inflammatory pain and discuss the prerequisites and opportunities to reduce attrition and high-failure rate in the development of analgesic drugs.

METHODS

A literature search was performed on preclinical and clinical publications aimed at the evaluation of analgesic compounds using MESH terms in PubMed. Publications were selected, which focused on (1) disease mechanisms leading to chronic/neuropathic pain and (2) druggable targets which are currently under evaluation in drug development. Attention was also given to the role of biomarkers and pharmacokinetic-pharmacodynamic modelling.

RESULTS

Multiple mechanisms act concurrently to produce pain, which is a non-specific manifestation of underlying nociceptive pathways. Whereas these manifestations can be divided into neuropathic and inflammatory pain, it is now clear that inflammatory mechanisms are a common trigger for both types of pain. This has implications for drug development, as the assessment of drug effects in experimental models of neuropathic and chronic pain is driven by overt behavioural measures. By contrast, the use of mechanistic biomarkers in inflammatory pain has provided the pharmacological basis for dose selection and evaluation of non-steroidal anti-inflammatory drugs (NSAIDs).

CONCLUSION

A different paradigm is required for the identification of relevant targets and candidate molecules whereby pain is coupled to the cause of sensorial signal processing dysfunction rather than clinical symptoms. Biomarkers which enable the characterisation of drug binding and target activity are needed for a more robust dose rationale in early clinical development. Such an approach may be facilitated by quantitative clinical pharmacology and evolving technologies in brain imaging, allowing accurate assessment of target engagement, and prediction of treatment effects before embarking on large clinical trials.

Data-intensive drug development in the information age: applications of Systems Biology/Pharmacology/Toxicology

Pharmaceutical companies continuously face challenges to deliver new drugs with true medical value. R&D productivity of drug development projects depends on 1) the value of the drug concept and 2) data and in-depth knowledge that are used rationally to evaluate the drug concept's validity. A model-based data-intensive drug development approach is a key competitive factor used by innovative pharmaceutical companies to reduce information bias and rationally demonstrate the value of drug concepts. Owing to the accumulation of publicly available biomedical information, our understanding of the pathophysiological mechanisms of diseases has developed considerably; it is the basis for identifying the right drug target and creating a drug concept with true medical value. Our understanding of the pathophysiological mechanisms of disease animal models can also be improved; it can thus support rational extrapolation of animal experiment results to clinical settings. The Systems Biology approach, which leverages publicly available transcriptome data, is useful for these purposes. Furthermore, applying Systems Pharmacology enables dynamic simulation of drug responses, from which key research questions to be addressed in the subsequent studies can be adequately informed. Application of Systems Biology/Pharmacology to toxicology research, namely Systems Toxicology, should considerably improve the predictability of drug-induced toxicities in clinical situations that are difficult to predict from conventional preclinical toxicology studies. Systems Biology/Pharmacology/Toxicology models can be continuously improved using iterative learn-confirm processes throughout preclinical and clinical drug discovery and development processes. Successful implementation of data-intensive drug development approaches requires cultivation of an adequate R&D culture to appreciate this approach.

The Impact of Mathematical Modeling in Understanding the Mechanisms Underlying Neurodegeneration: Evolving Dimensions and Future Directions

Neurodegenerative diseases are a heterogeneous group of disorders that are characterized by the progressive dysfunction and loss of neurons. Here, we distil and discuss the current state of modeling in the area of neurodegeneration, and objectively compare the gaps between existing clinical knowledge and the mechanistic understanding of the major pathological processes implicated in neurodegenerative disorders. We also discuss new directions in the field of neurodegeneration that hold potential for furthering therapeutic interventions and strategies.

Integration of pharmacometabolomics with pharmacokinetics and pharmacodynamics: towards personalized drug therapy

Personalized medicine, in modern drug therapy, aims at a tailored drug treatment accounting for inter-individual variations in drug pharmacology to treat individuals effectively and safely. The inter-individual variability in drug response upon drug administration is caused by the interplay between drug pharmacology and the patients' (patho)physiological status. Individual variations in (patho)physiological status may result from genetic polymorphisms, environmental factors (including current/past treatments), demographic characteristics, and disease related factors. Identification and quantification of predictors of inter-individual variability in drug pharmacology is necessary to achieve personalized medicine. Here, we highlight the potential of pharmacometabolomics in prospectively informing on the inter-individual differences in drug pharmacology, including both pharmacokinetic (PK) and pharmacodynamic (PD) processes, and thereby guiding drug selection and drug dosing. This review focusses on the pharmacometabolomics studies that have additional value on top of the conventional covariates in predicting drug PK. Additionally, employing pharmacometabolomics to predict drug PD is highlighted, and we suggest not only considering the endogenous metabolites as static variables but to include also drug dose and temporal changes in drug concentration in these studies. Although there are many endogenous metabolite biomarkers identified to predict PK and more often to predict PD, validation of these biomarkers in terms of specificity, sensitivity, reproducibility and clinical relevance is highly important. Furthermore, the application of these identified biomarkers in routine clinical practice deserves notable attention to truly personalize drug treatment in the near future.

Looking beyond the cancer cell for effective drug combinations

Combinations of therapies are being actively pursued to expand therapeutic options and deal with cancer’s pervasive resistance to treatment. Research efforts to discover effective combination treatments have focused on drugs targeting intracellular processes of the cancer cells and in particular on small molecules that target aberrant kinases. Accordingly, most of the computational methods used to study, predict, and develop drug combinations concentrate on these modes of action and signaling processes within the cancer cell. This focus on the cancer cell overlooks significant opportunities to tackle other components of tumor biology that may offer greater potential for improving patient survival. Many alternative strategies have been developed to combat cancer; for example, targeting different cancer cellular processes such as epigenetic control; modulating stromal cells that interact with the tumor; strengthening physical barriers that confine tumor growth; boosting the immune system to attack tumor cells; and even regulating the microbiome to support antitumor responses. We suggest that to fully exploit these treatment modalities using effective drug combinations it is necessary to develop multiscale computational approaches that take into account the full complexity underlying the biology of a tumor, its microenvironment, and a patient’s response to the drugs. In this Opinion article, we discuss preliminary work in this area and the needs—in terms of both computational and data requirements—that will truly empower such combinations.

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.

Multiscale Modeling in the Clinic: Drug Design and Development

A wide range of length and time scales are relevant to pharmacology, especially in drug development, drug design and drug delivery. Therefore, multiscale computational modeling and simulation methods and paradigms that advance the linkage of phenomena occurring at these multiple scales have become increasingly important. Multiscale approaches present in silico opportunities to advance laboratory research to bedside clinical applications in pharmaceuticals research. This is achievable through the capability of modeling to reveal phenomena occurring across multiple spatial and temporal scales, which are not otherwise readily accessible to experimentation. The resultant models, when validated, are capable of making testable predictions to guide drug design and delivery. In this review we describe the goals, methods, and opportunities of multiscale modeling in drug design and development. We demonstrate the impact of multiple scales of modeling in this field. We indicate the common mathematical and computational techniques employed for multiscale modeling approaches used in pharmacometric and systems pharmacology models in drug development and present several examples illustrating the current state-of-the-art models for (1) excitable systems and applications in cardiac disease; (2) stem cell driven complex biosystems; (3) nanoparticle delivery, with applications to angiogenesis and cancer therapy; (4) host-pathogen interactions and their use in metabolic disorders, inflammation and sepsis; and (5) computer-aided design of nanomedical systems. We conclude with a focus on barriers to successful clinical translation of drug development, drug design and drug delivery multiscale models.

Utilising the EGFR interactome to identify mechanisms of drug resistance in non-small cell lung cancer - Proof of concept towards a systems pharmacology approach

Drug treatment of epidermal growth factor receptor (EGFR) positive non-small cell lung cancer has improved substantially by targeting activating mutations within the receptor tyrosine kinase domain. However, the development of drug resistance still limits this approach. As root causes, large heterogeneity between tumour entities but also within tumour cells have been suggested. Therefore, approaches to identify these multitude and complex mechanisms are urgently required. Affinity purification coupled with high resolution mass spectrometry was applied to isolate and characterise the EGFR interactome from HCC4006 non-small cell lung cancer cells and their variant HCC4006^rERLO^0.5 adapted to grow in the presence of therapeutically relevant concentrations of erlotinib. Bioinformatics analyses were carried out to identify proteins and their related molecular functions that interact differentially with EGFR in the untreated state or when incubated with erlotinib prior to EGFR activation. Across all experimental conditions 375 proteins were detected to participate in the EGFR interactome, 90% of which constituted a complex protein interaction network that was bioinformatically reconstructed from literature data. Treatment of HCC4006^rERLO^0.5 cells carrying a resistance phenotype to erlotinib was associated with an increase of protein levels of members of the clathrin-associated adaptor protein family AP2 (AP2A1, AP2A2, AP2B1), structural proteins of cytoskeleton rearrangement as well as signalling molecules such as Shc. Validation experiments confirmed activation of the Ras-Raf-Mek-Erk (MAPK)-pathway, of which Shc is an initiating adaptor molecule, in HCC4006^rERLO^0.5 cells. Taken together, differential proteins in the EGFR interactome of HCC4006^rERLO^0.5 cells were identified that could be related to multiple resistance mechanisms including alterations in growth factor receptor expression, cellular remodelling processes suggesting epithelial-to-mesenchymal transition as well as alterations in downstream signalling. Knowledge of these mechanisms is a pivotal step to build an integrative model of drug resistance in a systems pharmacology manner and to be able to investigate the interplay of these mechanisms and ultimately recommend combinatorial treatment strategies to overcome drug resistance.