Moving from basic toward systems pharmacodynamic models

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

Over the past several decades, mathematical modeling has been applied to increasingly wider scopes of questions in drug development. Accordingly, the range of modeling tools has also been evolving, as showcased by contributions of Jusko and colleagues: from basic pharmacokinetics/pharmacodynamics (PK/PD) modeling to today's platform-based approach of quantitative systems pharmacology (QSP) modeling. Aimed at understanding the mechanism of action of investigational drugs, QSP models characterize systemic effects by incorporating information about cellular signaling networks, which is often represented by omics data. In this perspective, we share a few examples illustrating approaches for the integration of omics into mechanistic QSP modeling. We briefly overview how the evolution of PK/PD modeling into QSP has been accompanied by an increase in available data and the complexity of mathematical methods that integrate it. We discuss current gaps and challenges of integrating omics data into QSP models and propose several potential areas where integrated QSP and omics modeling may benefit drug development.

Pharmacodynamic Models of Indirect Effects and Irreversible Inactivation with Turnover: Applicability to Mechanism-Based Modeling of Gene Silencing and Targeted Protein Degradation

Indirect response (IDR) and turnover with inactivation (TI) comprise two arrays of mechanism-based pharmacodynamic (PD) models widely used to describe delayed drug effects. IDR Model-IV (stimulation of response loss) and TI (irreversible loss) have been described with discerning "signature" profiles; classical IDR-IV response-time profiles display slow declines where peak response shifts later with increasing dose, whereas TI profiles feature steep response declines with earlier-shifting nadirs. Herein, we demonstrate mathematical convergence of IDR-IV and TI models upon implementation with identical linear versus nonlinear pharmacologic effect terms. Time of peak response in IDR-IV can in fact shift earlier or later depending on PK or PD parameters (e.g., kel, Smax) and effect type. A generalized dynamic model linking mRNA and protein turnover is proposed. Applicability of IDR-IV and TI, with either linear or nonlinear terms acting on degradation/catabolism/loss of response, is demonstrated through model-fitting PK-PD effects of three proteolysis-targeting chimeras (PROTACs) and two ligand-conjugated small interfering RNAs (siRNA). This work clarifies mathematical properties, convergence, and expected responses of IDR-IV and TI, demonstrates their applicability for targeted gene-silencing and protein-degrading agents, and illustrates how well-designed in vivo studies covering broad dose ranges with richly sampled time-points can influence PK-PD model structure and parameter resolution.

Target Reserve and Turnover Parameters Determine Rightward Shift of Enalaprilat Potency From its Binding Affinity to the Angiotensin Converting Enzyme

Drug effects are often assumed to be directly proportional to the fraction of occupied targets. However, for a number of antagonists that exhibit target-mediated drug disposition (TMDD), such as angiotensin-converting enzyme (ACE) inhibitors, drug binding to the target at low concentrations may be significant enough to influence pharmacokinetics but insufficient to elicit a drug response (i.e., differences in drug-target binding affinity and potency). In this study, a pharmacokinetic/pharmacodynamic model for enalaprilat was developed in humans to provide a theoretical framework for assessing the relationship between ex vivo drug potency (IC50) and in vivo target-binding affinity (KD). The model includes competitive binding of angiotensin I and enalaprilat to ACE and accounts for the circulating target pool. Data were obtained from the literature, and model fitting and parameter estimation were conducted using maximum likelihood in ADAPT5. The model adequately characterized time-courses of enalaprilat concentrations and four biomarkers in the renin-angiotensin system and provided estimates for in vivo KD (0.646 nM) and system-specific parameters, such as total target density (32.0 nM) and fraction of circulating target (19.8%), which were in agreement with previous reports. Model simulations were used to predict the concentration-effect curve of enalaprilat, revealing a 6.3-fold increase in IC50 from KD. Additional simulations demonstrated that target reserve and degradation parameters are key factors determining the extent of shift of enalaprilat ex vivo potency from its in vivo binding affinity. This may be a common phenomenon for drugs exhibiting TMDD and has implications for translating binding affinity to potency in drug development.

Quantitative Raman chemical imaging of intracellular drug-membrane aggregates and small molecule drug precipitates in cytoplasmic organelles

Raman confocal microscopes have been used to visualize the distribution of small molecule drugs within different subcellular compartments. This visualization allows the discovery, characterization, and detailed analysis of the molecular transport phenomena underpinning the Volume of Distribution - a key parameter governing the systemic pharmacokinetics of small molecule drugs. In the specific case of lipophilic small molecules with large Volumes of Distribution, chemical imaging studies using Raman confocal microscopes have revealed how weakly basic, poorly soluble drug molecules can accumulate inside cells by forming stable, supramolecular complexes in association with cytoplasmic membranes or by precipitating out within organelles. To study the self-assembly and function of the resulting intracellular drug inclusions, Raman chemical imaging methods have been developed to measure and map the mass, concentration, and ionization state of drug molecules at a microscopic, subcellular level. Beyond the field of drug delivery, Raman chemical imaging techniques relevant to the study of microscopic drug precipitates and drug-lipid complexes which form inside cells are also being developed by researchers with seemingly unrelated scientific interests. Highlighting advances in data acquisition, calibration methods, and computational data management and analysis tools, this review will cover a decade of technological developments that enable the conversion of spectral signals obtained from Raman confocal microscopes into new discoveries and information about previously unknown, concentrative drug transport pathways driven by soluble-to-insoluble phase transitions occurring within the cytoplasmic organelles of eukaryotic cells.

In silico modeling of endocrine organ-on-a-chip systems

The organ-on-a-chip (OoC) is an artificially reconstructed microphysiological system that is implemented using tissue mimics integrated into miniaturized perfusion devices. OoCs emulate dynamic and physiologically relevant features of the body, which are not available in standard in vitro methods. Furthermore, OoCs provide highly sophisticated multi-organ connectivity and biomechanical cues based on microfluidic platforms. Consequently, they are often considered ideal in vitro systems for mimicking self-regulating biophysical and biochemical networks in vivo where multiple tissues and organs crosstalk through the blood flow, similar to the human endocrine system. Therefore, OoCs have been extensively applied to simulate complex hormone dynamics and endocrine signaling pathways in a mechanistic and fully controlled manner. Mathematical and computational modeling approaches are critical for quantitatively analyzing an OoC and predicting its complex responses. In this review article, recently developed in silico modeling concepts of endocrine OoC systems are summarized, including the mathematical models of tissue-level transport phenomena, microscale fluid dynamics, distant hormone signaling, and heterogeneous cell-cell communication. From this background, whole chip-level analytic approaches in pharmacokinetics and pharmacodynamics will be described with a focus on the spatial and temporal behaviors of absorption, distribution, metabolism, and excretion in endocrine biochips. Finally, quantitative design frameworks for endocrine OoCs are reviewed with respect to support parameter calibration/scaling and enable predictive in vitro-in vivo extrapolations. In particular, we highlight the analytical and numerical modeling strategies of the nonlinear phenomena in endocrine systems on-chip, which are of particular importance in drug screening and environmental health applications.

Towards a comprehensive assessment of QSP models: what would it take?

Quantitative Systems Pharmacology (QSP) has emerged as a powerful ensemble of approaches aiming at developing integrated mathematical and computational models elucidating the complex interactions between pharmacology, physiology, and disease. As the field grows and matures its applications expand beyond the boundaries of research and development and slowly enter the decision making and regulatory arenas. However, widespread acceptance and eventual adoption of a new modeling approach requires assessment criteria and quantifiable metrics that establish credibility and increase confidence in model predictions. QSP aims to provide an integrated understanding of pathology in the context of therapeutic interventions. Because of its ambitious nature and the fact that QSP emerged in an uncoordinated manner as a result of activities distributed across organizations and academic institutions, high entropy characterizes the tools, methods, and computational methodologies and approaches used. The eventual acceptance of QSP model predictions as supporting material for an application to a regulatory agency will require that two key aspects are considered: (1) increase confidence in the QSP framework, which drives standardization and assessment; and (2) careful articulation of the expectations. Both rely heavily on our ability to rigorously and consistently assess QSP models. In this manuscript, we wish to discuss the meaning and purpose of such an assessment in the context of QSP model development and elaborate on the differentiating features of QSP that render such an endeavor challenging. We argue that QSP establishes a conceptual, integrative framework rather than a specific and well-defined computational methodology. QSP elicits the use of a wide variety of modeling and computational methodologies optimized with respect to specific applications and available data modalities, which exceed the data structures employed by chemometrics and PK/PD models. While the range of options fosters creativity and promises to substantially advance our ability to design pharmaceutical interventions rationally and optimally, our expectations of QSP models need to be clearly articulated and agreed on, with assessment emphasizing the scope of QSP studies rather than the methods used. Nevertheless, QSP should not be considered an independent approach, rather one of many in the broader continuum of computational models.

Computational modeling of drug dissolution in the human stomach: Effects of posture and gastroparesis on drug bioavailability

The oral route is the most common choice for drug administration because of several advantages, such as convenience, low cost, and high patient compliance, and the demand and investment in research and development for oral drugs continue to grow. The rate of dissolution and gastric emptying of the dissolved active pharmaceutical ingredient (API) into the duodenum is modulated by gastric motility, physical properties of the pill, and the contents of the stomach, but current in vitro procedures for assessing dissolution of oral drugs are limited in their ability to recapitulate this process. This is particularly relevant for disease conditions, such as gastroparesis, that alter the anatomy and/or physiology of the stomach. In silico models of gastric biomechanics offer the potential for overcoming these limitations of existing methods. In the current study, we employ a biomimetic in silico simulator based on the realistic anatomy and morphology of the stomach (referred to as "StomachSim") to investigate and quantify the effect of body posture and stomach motility on drug bioavailability. The simulations show that changes in posture can potentially have a significant (up to 83%) effect on the emptying rate of the API into the duodenum. Similarly, a reduction in antral contractility associated with gastroparesis can also be found to significantly reduce the dissolution of the pill as well as emptying of the API into the duodenum. The simulations show that for an equivalent motility index, the reduction in gastric emptying due to neuropathic gastroparesis is larger by a factor of about five compared to myopathic gastroparesis.

Treatment with Living Drugs: Pharmaceutical Aspects of CAR T Cells

BACKGROUND

Adoptive therapy with genetically modified T cells achieves spectacular remissions in advanced hematologic malignancies. In contrast to conventional drugs, this kind of therapy applies viable autologous T cells that are ex vivo genetically engineered with a chimeric antigen receptor (CAR) and are classified as advanced therapy medicinal products.

SUMMARY

As "living drugs," CAR T cells differ from classical pharmaceutical drugs as they provide a panel of cellular capacities upon CAR signaling, including the release of effector molecules and cytokines, redirected cytotoxicity, CAR T cell amplification, active migration, and long-term persistence and immunological memory. Here, we discuss pharmaceutical aspects, the regulatory requirements for CAR T cell manufacturing, and how CAR T cell pharmacokinetics are connected with the clinical outcome.

KEY MESSAGES

From the pharmacological perspective, the development of CAR T cells with high translational potential needs to address pharmacodynamic markers to balance safety and efficacy of CAR T cells and to address pharmacokinetics with respect to trafficking, homing, infiltration, and persistence of CAR T cells.

Precision Cardio-Oncology: Use of Mechanistic Pharmacokinetic and Pharmacodynamic Modeling to Predict Cardiotoxicities of Anti-Cancer Drugs

The cardiotoxicity of anti-cancer drugs presents as a challenge to both clinicians and patients. Significant advances in cancer treatments have improved patient survival rates, but have also led to the chronic effects of anti-cancer therapies becoming more prominent. Additionally, it is difficult to clinically predict the occurrence of cardiovascular toxicities given that they can be transient or irreversible, with large between-subject variabilities. Further, cardiotoxicities present a range of different symptoms and pathophysiological mechanisms. These notwithstanding, mechanistic pharmacokinetic (PK) and pharmacodynamic (PD) modeling offers an important approach to predict cardiotoxicities and offering precise cardio-oncological care. Efforts have been made to integrate the structures of physiological and pharmacological networks into PK-PD modeling to the end of predicting cardiotoxicities based on clinical evaluation as well as individual variabilities, such as protein expression, and physiological changes under different disease states. Thus, this review aims to report recent progress in the use of PK-PD modeling to predict cardiovascular toxicities, as well as its application in anti-cancer therapies.

A Mechanistic Site-Of-Action Model: A Tool for Informing Right Target, Right Compound, And Right Dose for Therapeutic Antagonistic Antibody Programs

Quantitative modeling is increasingly utilized in the drug discovery and development process, from the initial stages of target selection, through clinical studies. The modeling can provide guidance on three major questions–is this the right target, what are the right compound properties, and what is the right dose for moving the best possible candidate forward. In this manuscript, we present a site-of-action modeling framework which we apply to monoclonal antibodies against soluble targets. We give a comprehensive overview of how we construct the model and how we parametrize it and include several examples of how to apply this framework for answering the questions postulated above. The utilities and limitations of this approach are discussed.

Development of a quantitative relationship between CAR-affinity, antigen abundance, tumor cell depletion and CAR-T cell expansion using a multiscale systems PK-PD model

The development of mechanism-based, multiscale pharmacokinetic–pharmacodynamic (PK-PD) models for chimeric antigen receptor (CAR)-T cells is needed to enable investigation of in vitro and in vivo correlation of CAR-T cell responses and to facilitate preclinical-to-clinical translation. Toward this goal, we first developed a cell-level in vitro PD model that quantitatively characterized CAR-T cell-induced target cell depletion, CAR-T cell expansion and cytokine release. The model accounted for key drug-specific (CAR-affinity, CAR-densities) and system-specific (antigen densities, E:T ratios) variables and was able to characterize comprehensive in vitro datasets from multiple affinity variants of anti-EGFR and anti-HER2 CAR-T cells. Next, a physiologically based PK (PBPK) model was developed to simultaneously characterize the biodistribution of untransduced T-cells, anti-EGFR CAR-T and anti-CD19 CAR-T cells in xenograft -mouse models. The proposed model accounted for the engagement of CAR-T cells with tumor cells at the site of action. Finally, an integrated PBPK-PD relationship was established to simultaneously characterize expansion of CAR-T cells and tumor growth inhibition (TGI) in xenograft mouse model, using datasets from anti-BCMA, anti-HER2, anti-CD19 and anti-EGFR CAR-T cells. Model simulations provided potential mechanistic insights toward the commonly observed multiphasic PK profile (i.e., rapid distribution, expansion, contraction and persistence) of CAR-T cells in the clinic. Model simulations suggested that CAR-T cells may have a steep dose-exposure relationship, and the apparent Cmax upon CAR-T cell expansion in blood may be more sensitive to patient tumor-burden than CAR-T dose levels. Global sensitivity analysis described the effect of other drug-specific parameters toward CAR-T cell expansion and TGI. The proposed modeling framework will be further examined with the clinical PK and PD data, and the learnings can be used to inform design and development of future CAR-T therapies.

Decoding (patho-)physiology of the lung by advanced in vitro models for developing novel anti-infectives therapies

Advanced lung cell culture models provide physiologically-relevant and complex data for mathematical models to exploit host-pathogen responses during anti-infective drug testing.

Transitioning from Basic toward Systems Pharmacodynamic Models: Lessons from Corticosteroids

Technology in bioanalysis, -omics, and computation have evolved over the past half century to allow for comprehensive assessments of the molecular to whole body pharmacology of diverse corticosteroids. Such studies have advanced pharmacokinetic and pharmacodynamic (PK/PD) concepts and models that often generalize across various classes of drugs. These models encompass the "pillars" of pharmacology, namely PK and target drug exposure, the mass-law interactions of drugs with receptors/targets, and the consequent turnover and homeostatic control of genes, biomarkers, physiologic responses, and disease symptoms. Pharmacokinetic methodology utilizes noncompartmental, compartmental, reversible, physiologic [full physiologically based pharmacokinetic (PBPK) and minimal PBPK], and target-mediated drug disposition models using a growing array of pharmacometric considerations and software. Basic PK/PD models have emerged (simple direct, biophase, slow receptor binding, indirect response, irreversible, turnover with inactivation, and transduction models) that place emphasis on parsimony, are mechanistic in nature, and serve as highly useful "top-down" methods of quantitating the actions of diverse drugs. These are often components of more complex quantitative systems pharmacology (QSP) models that explain the array of responses to various drugs, including corticosteroids. Progressively deeper mechanistic appreciation of PBPK, drug-target interactions, and systems physiology from the molecular (genomic, proteomic, metabolomic) to cellular to whole body levels provides the foundation for enhanced PK/PD to comprehensive QSP models. Our research based on cell, animal, clinical, and theoretical studies with corticosteroids have provided ideas and quantitative methods that have broadly advanced the fields of PK/PD and QSP modeling and illustrates the transition toward a global, systems understanding of actions of diverse drugs. SIGNIFICANCE STATEMENT: Over the past half century, pharmacokinetics (PK) and pharmacokinetics/pharmacodynamics (PK/PD) have evolved to provide an array of mechanism-based models that help quantitate the disposition and actions of most drugs. We describe how many basic PK and PK/PD model components were identified and often applied to the diverse properties of corticosteroids (CS). The CS have complications in disposition and a wide array of simple receptor-to complex gene-mediated actions in multiple organs. Continued assessments of such complexities have offered opportunities to develop models ranging from simple PK to enhanced PK/PD to quantitative systems pharmacology (QSP) that help explain therapeutic and adverse CS effects. Concurrent development of state-of-the-art PK, PK/PD, and QSP models are described alongside experimental studies that revealed diverse CS actions.

Maintenance Immunosuppression in Solid Organ Transplantation: Integrating Novel Pharmacodynamic Biomarkers to Inform Calcineurin Inhibitor Dose Selection

Calcineurin inhibitors, the primary immunosuppressive therapy used to prevent alloreactivity of transplanted organs, have a narrow therapeutic index. Currently, treatment is individualized based on clinical assessment of the risk of rejection or toxicity guided by trough concentration monitoring. Advances in immune monitoring have identified potential markers that may have value in understanding calcineurin inhibitor pharmacodynamics. Integration of these markers has the potential to complement therapeutic drug monitoring. Existing pharmacokinetic-pharmacodynamic (PK-PD) data is largely limited to correlation between the biomarker and trough concentrations at single time points. Immune related gene expression currently has the most evidence supporting PK-PD integration. Novel biomarker-based approaches to pharmacodynamic monitoring including development of enhanced PK-PD models are proposed to realize the full clinical benefit.

Integrated Multi-stakeholder Systems Thinking Strategy: Decision-making with Biopharmaceutics Risk Assessment Roadmap (BioRAM) to Optimize Clinical Performance of Drug Products

Decision-making in drug development benefits from an integrated systems approach, where the stakeholders identify and address the critical questions for the system through carefully designed and performed studies. Biopharmaceutics Risk Assessment Roadmap (BioRAM) is such a systems approach for application of systems thinking to patient focused and timely decision-making, suitable for all stages of drug discovery and development. We described the BioRAM therapy-driven drug delivery framework, strategic roadmap, and integrated risk assessment instrument (BioRAM Scoring Grid) in previous publications (J Pharm Sci 103:3377-97, 2014; J Pharm Sci 105:3243-55, 2016). Integration of systems thinking with pharmaceutical development, manufacturing, and clinical sciences and health care is unique to BioRAM where the developed strategy identifies the system and enables risk characterization and balancing for the entire system. Successful decision-making process in BioRAM starts with the Blueprint (BP) meetings. Through shared understanding of the system, the program strategy is developed and captured in the program BP. Here, we provide three semi-hypothetical examples for illustrating risk-based decision-making in high and moderate risk settings. In the high-risk setting, which is a rare disease area, two completely alternate development approaches are considered (gene therapy and small molecule). The two moderate-risk examples represent varied knowledge levels and drivers for the programs. In one moderate-risk example, knowledge leveraging opportunities are drawn from the manufacturing knowledge and clinical performance of a similar drug substance. In the other example, knowledge on acute tolerance patterns for a similar mechanistic pathway is utilized for identifying markers to inform the drug release profile from the dosage form with the necessary "flexibility" for dosing. All examples illustrate implementation of the BioRAM strategy for leveraging knowledge and decision-making to optimize the clinical performance of drug products for patient benefit.

Ethambutol disposition in humans: Challenges and limitations of whole-body physiologically-based pharmacokinetic modelling in early drug development

Whole-body physiologically based pharmacokinetic (WB-PBPK) models have become an important tool in drug development, as they enable characterization of pharmacokinetic profiles across different organs based on physiological (systems-specific) and physicochemical (drug-specific) properties. However, it remains unclear which data are needed for accurate predictions when applying the approach to novel candidate molecules progressing into the clinic. In this work, as case study, we investigated the predictive performance of WB-PBPK models both for prospective and retrospective evaluation of the pharmacokinetics of ethambutol, considering scenarios that reflect different stages of development, including settings in which the data are limited to in vitro experiments, in vivo preclinical data, and when some clinical data are available. Overall, the accuracy of PBPK model-predicted systemic and tissue exposure was heavily dependant on prior knowledge about the eliminating organs. Whilst these findings may be specific to ethambutol, the challenges and potential limitations identified here may be relevant to a variety of drugs, raising questions about (1) the minimum requirements for prospective use of WB-PBPK models during the characterization of drug disposition and (2) implication of uncertainty for dose selection in humans.

Entering the era of computationally driven drug development

Historically, failure rates in drug development are high; increased sophistication and investment throughout the process has shifted the reasons for attrition, but the overall success rates have remained stubbornly and consistently low. Only 8% of new entities entering clinical testing gain regulatory approval, indicating that significant obstacles still exist for efficient therapeutic development. The continued high failure rate can be partially attributed to the inability to link drug exposure with the magnitude of observed safety and efficacy-related pharmacodynamic (PD) responses; frequently, this is a result of nonclinical models exhibiting poor prediction of human outcomes across a wide range of disease conditions, resulting in faulty evaluation of drug toxicology and efficacy. However, the increasing quality and standardization of experimental methods in preclinical stages of testing has created valuable data sets within companies that can be leveraged to further improve the efficiency and accuracy of preclinical prediction for both pharmacokinetics (PK) and PD. Models of Quantitative structure-activity relationships (QSAR), physiologically based pharmacokinetics (PBPK), and PK/PD relationships have also improved efficiency. Founded on a core understanding of biochemistry and physiological interactions of xenobiotics, these in silico methods have the potential to increase the probability of compound success in clinical trials. Integration of traditional computational methods with machine-learning approaches and existing internal pharma databases stands to make a fundamental impact on the speed and accuracy of predictions during the process of drug development and approval.

ATLAS mPBPK: A MATLAB-Based Tool for Modeling and Simulation of Minimal Physiologically-Based Pharmacokinetic Models

Minimal physiologically-based pharmacokinetic (mPBPK) models are frequently used to model plasma pharmacokinetic (PK) data and utilize and yield physiologically relevant parameters. Compared with classical compartment and whole-body physiologically-based pharmacokinetic modeling approaches, mPBPK models maintain a structure of intermediate physiological complexity that can be adequately informed by plasma PK data. In this tutorial, we present a MATLAB-based tool for the modeling and simulation of mPBPK models (ATLAS mPBPK) of small and large molecules. This tool enables the users to perform the following: (i) PK data visualization, (ii) simulation, (iii) parameter optimization, and (iv) local sensitivity analysis of mPBPK models in a simple and efficient manner. In addition to the theoretical background and implementation of the different tool functionalities, this tutorial includes simulation and sensitivity analysis showcases of small and large molecules with and without target-mediated drug disposition.

Pharmacokinetics and pharmacodynamics of inhaled corticosteroids for asthma treatment

The differences in the pharmacokinetic (PK) characteristics of inhaled corticosteroids (ICSs) critically influence the profile of each of them, but also the significant differences in glucocorticoid receptor selectivity, potency, and physicochemical properties are critical in defining the pharmacodynamic (PD) profile of an ICS. The PK and PD properties of ICSs used in asthma and the importance of their interrelationship have been reviewed. The differences among the ICSs in PK and PD must be considered when an ICS should be prescribed to an asthmatic patient because a better understanding of the PK/PD interrelationship of ICSs could be important to better fit with the between-patient variability and within-patient repeatability in the response to ICSs that often complicate the therapeutic approach to the asthmatic patient. The role of the device in influencing the PK profile of an ICS must be always considered because it is crucial. Also patient-related factors and disease severity affect pulmonary deposition of ICS.

Application of biomimetic HPLC to estimate in vivo behavior of early drug discovery compounds

Characterizing the properties of large numbers of compounds and estimating their potential absorption, distribution, metabolism and elimination properties are important early stages in the process of drug discovery and help to reduce later stage attrition. The chromatographic separation principles using stationary phases that contain proteins and phospholipids are more suitable for compound characterization and estimation of the pharmacokinetic properties than the traditional octanol/water partition coefficient. This technology, when standardized, enables the prediction of in vivo behavior and the selection of compounds with the best potential, thus reducing the number of animal experiments. Chromatography may be involved more widely in the future to measure kinetic aspects of compounds’ binding to proteins and receptors which would enable designing compounds that require a lower frequency of doses and have more predictable pharmacokinetic profiles.

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.

Therapeutic targeting of the NRF2 and KEAP1 partnership in chronic diseases

The transcription factor NF-E2 p45-related factor 2 (NRF2; encoded by NFE2L2) and its principal negative regulator, the E3 ligase adaptor Kelch-like ECH-associated protein 1 (KEAP1), are critical in the maintenance of redox, metabolic and protein homeostasis, as well as the regulation of inflammation. Thus, NRF2 activation provides cytoprotection against numerous pathologies including chronic diseases of the lung and liver; autoimmune, neurodegenerative and metabolic disorders; and cancer initiation. One NRF2 activator has received clinical approval and several electrophilic modifiers of the cysteine-based sensor KEAP1 and inhibitors of its interaction with NRF2 are now in clinical development. However, challenges regarding target specificity, pharmacodynamic properties, efficacy and safety remain.

Glucocorticoids pharmacology and their application in the treatment of childhood-onset systemic lupus erythematosus

Glucocorticoids are potent anti-inflammatory and immunosuppressant medications and remain the mainstay of systemic lupus erythematosus (SLE) therapy. The potency of a specific glucocorticoid, i.e., the dose of glucocorticoid that is required to produce a specific effect, is dependent on its pharmacokinetic (PK) and pharmacodynamic (PD) properties. In this review, we summarize the PK/PD properties of commonly used glucocorticoids in an attempt to better delineate their role in the management of children with childhood-onset SLE (cSLE). We also address glucocorticoid side effects as these play a major role when deciding on the dose, frequency, and duration of use. A better understanding of the pharmacology of glucocorticoids appears useful to achieve improved outcomes in the management of cSLE.

Application of the relationship between pharmacokinetics and pharmacodynamics in drug development and therapeutic equivalence: a PEARRL review

Objectives

The objective of this review was to provide an overview of pharmacokinetic/pharmacodynamic (PK/PD) models, focusing on drug-specific PK/PD models and highlighting their value added in drug development and regulatory decision-making.

Key findings

Many PK/PD models, with varying degrees of complexity and physiological understanding have been developed to evaluate the safety and efficacy of drug products. In special populations (e.g. paediatrics), in cases where there is genetic polymorphism and in other instances where therapeutic outcomes are not well described solely by PK metrics, the implementation of PK/PD models is crucial to assure the desired clinical outcome. Since dissociation between the pharmacokinetic and pharmacodynamic profiles is often observed, it is proposed that physiologically based pharmacokinetic and PK/PD models be given more weight by regulatory authorities when assessing the therapeutic equivalence of drug products.

Summary

Modelling and simulation approaches already play an important role in drug development. While slowly moving away from ‘one-size fits all’ PK methodologies to assess therapeutic outcomes, further work is required to increase confidence in PK/PD models in translatability and prediction of various clinical scenarios to encourage more widespread implementation in regulatory decision-making.

Mechanism-Based Disease Progression Model Describing Host-Pathogen Interactions During the Pathogenesis of Acinetobacter baumannii Pneumonia

The emergence of highly resistant bacteria is a serious threat to global public health. The host immune response is vital for clearing bacteria from the infected host; however, the current drug development paradigm does not take host‐pathogen interactions into consideration. Here, we used a systems‐based approach to develop a quantitative, mechanism‐based disease progression model to describe bacterial dynamics, host immune response, and lung injury in an immunocompetent rat pneumonia model. Previously, Long‐Evans rats were infected with Acinetobacter baumannii (A. baumannii) strain 307‐0294 at five different inocula and total lung bacteria, interleukin‐1beta (IL‐1β), tumor necrosis factor‐α (TNF‐α), cytokine‐induced neutrophil chemoattractant 1 (CINC‐1), neutrophil counts, and albumin were quantified. Model development was conducted in ADAPT5 version 5.0.54 using a pooled approach with maximum likelihood estimation; all data were co‐modeled. The final model characterized host‐pathogen interactions during the natural time course of bacterial pneumonia. Parameters were estimated with good precision. Our expandable model will integrate drug effects to aid in the design of optimized antibiotic regimens.

Modeling Corticosteroid Pharmacogenomics and Proteomics in Rat Liver

Corticosteroids (CS) regulate the expression of numerous genes at the mRNA and protein levels. The time course of CS pharmacogenomics and proteomics were examined in livers obtained from adrenalectomized rats given a 50-mg/kg bolus dose of methylprednisolone. Microarrays and mass spectrometry-based proteomics were employed to quantify hepatic transcript and protein dynamics. One-hundred, sixty-three differentially expressed mRNA and their corresponding proteins (163 genes) were clustered into two dominant groups. The temporal profiles of most proteins were delayed compared with their mRNA, attributable to synthesis delays and slower degradation kinetics. On the basis of our fifth-generation model of CS, mathematical models were developed to simultaneously describe the emergent time patterns for an array of steroid-responsive mRNA and proteins. The majority of genes showed time-dependent increases in mRNA and protein expression before returning to baseline. A model assuming direct, steroid-mediated stimulation of mRNA synthesis was applied. Some mRNAs and their proteins displayed down-regulation following CS. A model assuming receptor-mediated inhibition of mRNA synthesis was used. More complex patterns were observed for other genes (e.g., biphasic behaviors and opposite directionality in mRNA and protein). Models assuming either stimulation or inhibition of mRNA synthesis coupled with dual secondarily induced regulatory mechanisms affecting mRNA or protein turnover were derived. These findings indicate that CS-regulated gene expression manifested at the mRNA and protein levels are controlled via mechanisms affecting key turnover processes. Our quantitative models of CS pharmacogenomics were expanded from mRNA to proteins and provide extended hypotheses for understanding the direct, secondary, and downstream mechanisms of CS actions.

Receptor/gene/protein-mediated signaling connects methylprednisolone exposure to metabolic and immune-related pharmacodynamic actions in liver

A multiscale pharmacodynamic model was developed to characterize the receptor-mediated, transcriptomic, and proteomic determinants of corticosteroid (CS) effects on clinically relevant hepatic processes following a single dose of methylprednisolone (MPL) given to adrenalectomized (ADX) rats. The enhancement of tyrosine aminotransferase (TAT) mRNA, protein, and enzyme activity were simultaneously described. Mechanisms related to the effects of MPL on glucose homeostasis, including the regulation of CCAAT-enhancer binding protein-beta (C/EBPβ) and phosphoenolpyruvate carboxykinase (PEPCK) as well as insulin dynamics were evaluated. The MPL-induced suppression of circulating lymphocytes was modeled by coupling its effect on cell trafficking with pharmacogenomic effects on cell apoptosis via the hepatic (STAT3-regulated) acute phase response. Transcriptomic and proteomic time-course profiles measured in steroid-treated rat liver were utilized to model the dynamics of mechanistically relevant gene products, which were linked to associated systemic end-points. While time-courses of TAT mRNA, protein, and activity were well described by transcription-mediated changes, additional post-transcriptional processes were included to explain the lack of correlation between PEPCK mRNA and protein. The immune response model quantitatively discerned the relative roles of cell trafficking versus gene-mediated lymphocyte apoptosis by MPL. This systems pharmacodynamic model provides insights into the contributions of selected molecular events occurring in liver and explores mechanistic hypotheses for the multi-factorial control of clinically relevant pharmacodynamic outcomes.

Development and validation of a physiology-based model for the prediction of pharmacokinetics/toxicokinetics in rabbits

The environmental fates of pharmaceuticals and the effects of crop protection products on non-target species are subjects that are undergoing intense review. Since measuring the concentrations and effects of xenobiotics on all affected species under all conceivable scenarios is not feasible, standard laboratory animals such as rabbits are tested, and the observed adverse effects are translated to focal species for environmental risk assessments. In that respect, mathematical modelling is becoming increasingly important for evaluating the consequences of pesticides in untested scenarios. In particular, physiologically based pharmacokinetic/toxicokinetic (PBPK/TK) modelling is a well-established methodology used to predict tissue concentrations based on the absorption, distribution, metabolism and excretion of drugs and toxicants. In the present work, a rabbit PBPK/TK model is developed and evaluated with data available from the literature. The model predictions include scenarios of both intravenous (i.v.) and oral (p.o.) administration of small and large compounds. The presented rabbit PBPK/TK model predicts the pharmacokinetics (Cmax, AUC) of the tested compounds with an average 1.7-fold error. This result indicates a good predictive capacity of the model, which enables its use for risk assessment modelling and simulations.

Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models

A systems pharmacology model typically integrates pharmacokinetic, biochemical network, and systems biology concepts into a unifying approach. It typically consists of a large number of parameters and reaction species that are interlinked based upon the underlying (patho)physiology and the mechanism of drug action. The more complex these models are, the greater the challenge of reliably identifying and estimating respective model parameters. Global sensitivity analysis provides an innovative tool that can meet this challenge. CPT Pharmacometrics Syst. Pharmacol. (2015) 4, 69-79; doi:10.1002/psp4.6; published online 25 February 2015.

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.

Bayesian state space models for dynamic genetic network construction across multiple tissues

Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

Quantitative systems toxicology

The overarching goal of modern drug development is to optimize therapeutic benefits while minimizing adverse effects. However, inadequate efficacy and safety concerns remain to be the major causes of drug attrition in clinical development. For the past 80 years, toxicity testing has consisted of evaluating the adverse effects of drugs in animals to predict human health risks. The U.S. Environmental Protection Agency recognized the need to develop innovative toxicity testing strategies and asked the National Research Council to develop a long-range vision and strategy for toxicity testing in the 21st century. The vision aims to reduce the use of animals and drug development costs through the integration of computational modeling and in vitro experimental methods that evaluates the perturbation of toxicity-related pathways. Towards this vision, collaborative quantitative systems pharmacology and toxicology modeling endeavors (QSP/QST) have been initiated amongst numerous organizations worldwide. In this article, we discuss how quantitative structure-activity relationship (QSAR), network-based, and pharmacokinetic/pharmacodynamic modeling approaches can be integrated into the framework of QST models. Additionally, we review the application of QST models to predict cardiotoxicity and hepatotoxicity of drugs throughout their development. Cell and organ specific QST models are likely to become an essential component of modern toxicity testing, and provides a solid foundation towards determining individualized therapeutic windows to improve patient safety.

Mechanistic Multi-Tissue Modeling of Glucocorticoid-Induced Leucine Zipper Regulation: Integrating Circadian Gene Expression with Receptor-Mediated Corticosteroid Pharmacodynamics

The glucocorticoid-induced leucine zipper (GILZ) is an important mediator of anti-inflammatory corticosteroid action. The pharmacokinetic/pharmacodynamic/pharmacogenomic effects of acute and chronic methylprednisolone (MPL) dosing on the tissue-specific dynamics of GILZ expression were examined in rats. A mechanism-based model was developed to investigate and integrate the role of MPL and circadian rhythms on the transcriptional enhancement of GILZ in multiple tissues. Animals received a single 50-mg/kg intramuscular bolus or a 7-day 0.3-mg/kg/h subcutaneous infusion of MPL and were euthanized at several time points. An additional group of rats were euthanized at several times and served as 24-hour light/dark (circadian) controls. Plasma MPL and corticosterone concentrations were measured by high-performance liquid chromatography. The expression of GILZ and glucocorticoid receptor (GR) mRNA was quantified in tissues using quantitative real-time reverse-transcription polymerase chain reaction. The pharmacokinetics of MPL were described using a two-compartment model. Mild-to-robust circadian oscillations in GR and GILZ mRNA expression were characterized in muscle, lung, and adipose tissues and modeled using Fourier harmonic functions. Acute MPL dosing caused significant down-regulation (40%-80%) in GR mRNA and enhancement of GILZ mRNA expression (500%-1080%) in the tissues examined. While GILZ returned to its rhythmic baseline following acute dosing, a new steady-state was observed upon enhancement by chronic dosing. The model captured the complex dynamics in all tissues for both dosing regimens. The model quantitatively integrates physiologic mechanisms, such as circadian processes and GR tolerance phenomena, which control the tissue-specific regulation of GILZ by corticosteroids. These studies characterize GILZ as a pharmacodynamic marker of corticosteroid actions in several tissues.

Drug-disease modeling in the pharmaceutical industry - where mechanistic systems pharmacology and statistical pharmacometrics meet

Modeling & simulation (M&S) methodologies are established quantitative tools, which have proven to be useful in supporting the research, development (R&D), regulatory approval, and marketing of novel therapeutics. Applications of M&S help design efficient studies and interpret their results in context of all available data and knowledge to enable effective decision-making during the R&D process. In this mini-review, we focus on two sets of modeling approaches: population-based models, which are well-established within the pharmaceutical industry today, and fall under the discipline of clinical pharmacometrics (PMX); and systems dynamics models, which encompass a range of models of (patho-)physiology amenable to pharmacological intervention, of signaling pathways in biology, and of substance distribution in the body (today known as physiologically-based pharmacokinetic models) - which today may be collectively referred to as quantitative systems pharmacology models (QSP). We next describe the convergence - or rather selected integration - of PMX and QSP approaches into 'middle-out' drug-disease models, which retain selected mechanistic aspects, while remaining parsimonious, fit-for-purpose, and able to address variability and the testing of covariates. We further propose development opportunities for drug-disease systems models, to increase their utility and applicability throughout the preclinical and clinical spectrum of pharmaceutical R&D.

Functional proteomic analysis of corticosteroid pharmacodynamics in rat liver: Relationship to hepatic stress, signaling, energy regulation, and drug metabolism

Corticosteroids (CS) are anti-inflammatory agents that cause extensive pharmacogenomic and proteomic changes in multiple tissues. An understanding of the proteome-wide effects of CS in liver and its relationships to altered hepatic and systemic physiology remains incomplete. Here, we report the application of a functional pharmacoproteomic approach to gain integrated insight into the complex nature of CS responses in liver in vivo. An in-depth functional analysis was performed using rich pharmacodynamic (temporal-based) proteomic data measured over 66h in rat liver following a single dose of methylprednisolone (MPL). Data mining identified 451 differentially regulated proteins. These proteins were analyzed on the basis of temporal regulation, cellular localization, and literature-mined functional information. Of the 451 proteins, 378 were clustered into six functional groups based on major clinically-relevant effects of CS in liver. MPL-responsive proteins were highly localized in the mitochondria (20%) and cytosol (24%). Interestingly, several proteins were related to hepatic stress and signaling processes, which appear to be involved in secondary signaling cascades and in protecting the liver from CS-induced oxidative damage. Consistent with known adverse metabolic effects of CS, several rate-controlling enzymes involved in amino acid metabolism, gluconeogenesis, and fatty-acid metabolism were altered by MPL. In addition, proteins involved in the metabolism of endogenous compounds, xenobiotics, and therapeutic drugs including cytochrome P450 and Phase-II enzymes were differentially regulated. Proteins related to the inflammatory acute-phase response were up-regulated in response to MPL. Functionally-similar proteins showed large diversity in their temporal profiles, indicating complex mechanisms of regulation by CS.

SIGNIFICANCE

Clinical use of corticosteroid (CS) therapy is frequent and chronic. However, current knowledge on the proteome-level effects of CS in liver and other tissues is sparse. While transcriptomic regulation following methylprednisolone (MPL) dosing has been temporally examined in rat liver, proteomic assessments are needed to better characterize the tissue-specific functional aspects of MPL actions. This study describes a functional pharmacoproteomic analysis of dynamic changes in MPL-regulated proteins in liver and provides biological insight into how steroid-induced perturbations on a molecular level may relate to both adverse and therapeutic responses presented clinically.

Understanding Physiology in the Continuum: Integration of Information from Multiple -Omics Levels

In this paper, we discuss approaches for integrating biological information reflecting diverse physiologic levels. In particular, we explore statistical and model-based methods for integrating transcriptomic, proteomic and metabolomics data. Our case studies reflect responses to a systemic inflammatory stimulus and in response to an anti-inflammatory treatment. Our paper serves partly as a review of existing methods and partly as a means to demonstrate, using case studies related to human endotoxemia and response to methylprednisolone (MPL) treatment, how specific questions may require specific methods, thus emphasizing the non-uniqueness of the approaches. Finally, we explore novel ways for integrating -omics information with PKPD models, toward the development of more integrated pharmacology models.

From an indirect response pharmacodynamic model towards a secondary signal model of dose-response relationship between exercise training and physical performance

The aim of this study was to test the suitability of using indirect responses for modeling the effects of physical training on performance. We formulated four different models assuming that increase in performance results of the transformation of a signal secondary to the primary stimulus which is the training dose. The models were designed to be used with experimental data with daily training amounts ascribed to input and performance measured at several dates ascribed to output. The models were tested using data obtained from six subjects who trained on a cycle ergometer over a 15-week period. The data fit for each subject was good for all of the models. Goodness-of-fit and consistency of parameter estimates favored the model that took into account the inhibition of production of training effect. This model produced an inverted-U shape graphic when plotting daily training dose against performance because of the effect of one training session on the cumulated effects of previous sessions. In conclusion, using secondary signal-dependent response provided a framework helpful for modeling training effect which could enhance the quantitative methods used to analyze how best to dose physical activity for athletic performance or healthy living.

Medication use and the risk of motor vehicle collisions among licensed drivers: A systematic review

OBJECTIVES

Driving under the influence of prescription and over-the-counter medication is a growing public health concern. A systematic review of the literature was performed to investigate which specific medications were associated with increased risk of motor vehicle collision (MVC).

METHODS

The a priori inclusion criteria were: (1) studies published from English-language sources on or after January 1, 1960, (2) licensed drivers 15 years of age and older, (3) peer-reviewed publications, master's theses, doctoral dissertations, and conference papers, (4) studies limited to randomized control trials, cohort studies, case-control studies, or case-control type studies (5) outcome measure reported for at least one specific medication, (6) outcome measure reported as the odds or risk of a motor vehicle collision. Fourteen databases were examined along with hand-searching. Independent, dual selection of studies and data abstraction was performed.

RESULTS

Fifty-three medications were investigated by 27 studies included in the review. Fifteen (28.3%) were associated with an increased risk of MVC. These included Buprenorphine, Codeine, Dihydrocodeine, Methadone, Tramadol, Levocitirizine, Diazepam, Flunitrazepam, Flurazepam, Lorazepam, Temazepam, Triazolam, Carisoprodol, Zolpidem, and Zopiclone.

CONCLUSIONS

Several medications were associated with an increased risk of MVC and decreased driving ability. The associations between specific medication use and the increased risk of MVC and/or affected driving ability are complex. Future research opportunities are plentiful and worthy of such investigation.

Time-Dependent Effects in Algae for Chemicals with Different Adverse Outcome Pathways: A Novel Approach

Chemicals affect unicellular algae as a result of toxicokinetic and toxicodynamic processes. The internal concentration of chemicals in algae cells typically reaches equilibrium within minutes, while damage cumulatively increases over hours. The time gap between the steady state of internal exposure and damage development is thus suspected to span up to hours, mainly due to toxicodynamic processes. The quantification of rate-limited toxicodynamic processes, aggregated as a progressive effect from an initiating molecular event through biological key events toward the adverse outcome on algae growth inhibition, might discriminate between different adverse outcome pathways (AOPs). To support our hypothesis, we selected six chemicals according to different physicochemical properties and three distinctly dissimilar AOPs. The time courses of internal concentrations were linked to the observed affected Scenedesmus vacuolatus growth using toxicokinetic-toxicodynamic modeling. Effects on cell growth were explained by effect progression and not by the time to reach internal equilibrium concentration. Effect progression rates ranged over 6 orders of magnitude for all chemicals but varied by less than 1 order of magnitude within similar AOP (photosystem II inhibitors > reactive chemicals > lipid biosynthesis inhibitors), meaning that inhibitors of photosystem II advance an effect toward algae growth fastest compared to reactive chemicals and inhibitors of lipid biosynthesis.

Multiscale modelling approaches for assessing cosmetic ingredients safety

The European Union's ban on animal testing for cosmetic ingredients and products has generated a strong momentum for the development of in silico and in vitro alternative methods. One of the focus of the COSMOS project was ab initio prediction of kinetics and toxic effects through multiscale pharmacokinetic modeling and in vitro data integration. In our experience, mathematical or computer modeling and in vitro experiments are complementary. We present here a summary of the main models and results obtained within the framework of the project on these topics. A first section presents our work at the organelle and cellular level. We then go toward modeling cell levels effects (monitored continuously), multiscale physiologically based pharmacokinetic and effect models, and route to route extrapolation. We follow with a short presentation of the automated KNIME workflows developed for dissemination and easy use of the models. We end with a discussion of two challenges to the field: our limited ability to deal with massive data and complex computations.

Quantitative Systems Pharmacology: A Framework for Context

Quantitative Systems Pharmacology (QSP) is receiving increased attention. As the momentum builds and the expectations grow it is important to (re)assess and formalize the basic concepts and approaches. In this short review, I argue that QSP, in addition to enabling the rational integration of data and development of complex models, maybe more importantly, provides the foundations for developing an integrated framework for the assessment of drugs and their impact on disease within a broader context expanding the envelope to account in great detail for physiology, environment and prior history. I articulate some of the critical enablers, major obstacles and exciting opportunities manifesting themselves along the way. Charting such overarching themes will enable practitioners to identify major and defining factors as the field progressively moves towards personalized and precision health care delivery.

A model qualification method for mechanistic physiological QSP models to support model-informed drug development

Mechanistic physiological modeling is a scientific method that combines available data with scientific knowledge and engineering approaches to facilitate better understanding of biological systems, improve decision‐making, reduce risk, and increase efficiency in drug discovery and development. It is a type of quantitative systems pharmacology (QSP) approach that places drug‐specific properties in the context of disease biology. This tutorial provides a broadly applicable model qualification method (MQM) to ensure that mechanistic physiological models are fit for their intended purposes.

Pharmacokinetic and pharmacodynamic considerations for the next generation protein therapeutics

Increasingly sophisticated protein engineering efforts have been undertaken lately to generate protein therapeutics with desired properties. This has resulted in the discovery of the next generation of protein therapeutics, which include: engineered antibodies, immunoconjugates, bi/multi-specific proteins, antibody mimetic novel scaffolds, and engineered ligands/receptors. These novel protein therapeutics possess unique physicochemical properties and act via a unique mechanism-of-action, which collectively makes their pharmacokinetics (PK) and pharmacodynamics (PD) different than other established biological molecules. Consequently, in order to support the discovery and development of these next generation molecules, it becomes important to understand the determinants controlling their PK/PD. This review discusses the determinants that a PK/PD scientist should consider during the design and development of next generation protein therapeutics. In addition, the role of systems PK/PD models in enabling rational development of the next generation protein therapeutics is emphasized.

Logic-Based and Cellular Pharmacodynamic Modeling of Bortezomib Responses in U266 Human Myeloma Cells

Systems models of biological networks show promise for informing drug target selection/qualification, identifying lead compounds and factors regulating disease progression, rationalizing combinatorial regimens, and explaining sources of intersubject variability and adverse drug reactions. However, most models of biological systems are qualitative and are not easily coupled with dynamical models of drug exposure-response relationships. In this proof-of-concept study, logic-based modeling of signal transduction pathways in U266 multiple myeloma (MM) cells is used to guide the development of a simple dynamical model linking bortezomib exposure to cellular outcomes. Bortezomib is a commonly used first-line agent in MM treatment; however, knowledge of the signal transduction pathways regulating bortezomib-mediated cell cytotoxicity is incomplete. A Boolean network model of 66 nodes was constructed that includes major survival and apoptotic pathways and was updated using responses to several chemical probes. Simulated responses to bortezomib were in good agreement with experimental data, and a reduction algorithm was used to identify key signaling proteins. Bortezomib-mediated apoptosis was not associated with suppression of nuclear factor κB (NFκB) protein inhibition in this cell line, which contradicts a major hypothesis of bortezomib pharmacodynamics. A pharmacodynamic model was developed that included three critical proteins (phospho-NFκB, BclxL, and cleaved poly (ADP ribose) polymerase). Model-fitted protein dynamics and cell proliferation profiles agreed with experimental data, and the model-predicted IC50 (3.5 nM) is comparable to the experimental value (1.5 nM). The cell-based pharmacodynamic model successfully links bortezomib exposure to MM cellular proliferation via protein dynamics, and this model may show utility in exploring bortezomib-based combination regimens.

Incorporation of stochastic variability in mechanistic population pharmacokinetic models: handling the physiological constraints using normal transformations

The utilisation of physiologically-based pharmacokinetic models for the analysis of population data is an approach with progressively increasing impact. However, as we move from empirical to complex mechanistic model structures, incorporation of stochastic variability in model parameters can be challenging due to the physiological constraints that may arise. Here, we investigated the most common types of constraints faced in mechanistic pharmacokinetic modelling and explored techniques for handling them during a population data analysis. An efficient way to impose stochastic variability on the parameters of interest without neglecting the underlying physiological constraints is through the assumption that they follow a distribution with support and properties matching the underlying physiology. It was found that two distributions that arise through transformations of the normal, the logit-normal generalisation and the logistic-normal, are excellent for such an application as not only they can satisfy the physiological constraints but also offer high flexibility during characterisation of the parameters' distribution. The statistical properties and practical advantages/disadvantages of these distributions for such an application were clearly displayed in the context of different modelling examples. Finally, a simulation study clearly illustrated the practical gains of the utilisation of the described techniques, as omission of population variability in physiological systems parameters leads to a biased/misplaced stochastic model with mechanistically incorrect variance structure. The current methodological work aims to facilitate the use of mechanistic/physiologically-based models for the analysis of population pharmacokinetic clinical data.

Parsing interindividual drug variability: an emerging role for systems pharmacology

There is notable interindividual heterogeneity in drug response, affecting both drug efficacy and toxicity, resulting in patient harm and the inefficient utilization of limited healthcare resources. Pharmacogenomics is at the forefront of research to understand interindividual drug response variability, but although many genotype-drug response associations have been identified, translation of pharmacogenomic associations into clinical practice has been hampered by inconsistent findings and inadequate predictive values. These limitations are in part due to the complex interplay between drug-specific, human body and environmental factors influencing drug response and therefore pharmacogenomics, whilst intrinsically necessary, is by itself unlikely to adequately parse drug variability. The emergent, interdisciplinary and rapidly developing field of systems pharmacology, which incorporates but goes beyond pharmacogenomics, holds significant potential to further parse interindividual drug variability. Systems pharmacology broadly encompasses two distinct research efforts, pharmacologically-orientated systems biology and pharmacometrics. Pharmacologically-orientated systems biology utilizes high throughput omics technologies, including next-generation sequencing, transcriptomics and proteomics, to identify factors associated with differential drug response within the different levels of biological organization in the hierarchical human body. Increasingly complex pharmacometric models are being developed that quantitatively integrate factors associated with drug response. Although distinct, these research areas complement one another and continual development can be facilitated by iterating between dynamic experimental and computational findings. Ultimately, quantitative data-derived models of sufficient detail will be required to help realize the goal of precision medicine.

Systems approach to the study of brain damage in the very preterm newborn

Background: A systems approach to the study of brain damage in very preterm newborns has been lacking.

Methods: In this perspective piece, we offer encephalopathy of prematurity as an example of the complexity and interrelatedness of brain-damaging molecular processes that can be initiated inflammatory phenomena.

Results: Using three transcription factors, nuclear factor-kappa B (NF-κB), Notch-1, and nuclear factor erythroid 2 related factor 2 (NRF2), we show the inter-connectedness of signaling pathways activated by some antecedents of encephalopathy of prematurity.

Conclusions: We hope that as biomarkers of exposures and processes leading to brain damage in the most immature newborns become more readily available, those who apply a systems approach to the study of neuroscience can be persuaded to study the pathogenesis of brain disorders in the very preterm newborn.