Development of translational pharmacokinetic-pharmacodynamic models. Clin Pharmacol Ther. 2008;83:909-912. [PMID: 18388873 DOI: 10.1038/clpt.2008.52]

A quantitative systems pharmacology (QSP) platform for preclinical to clinical translation of in-vivo CRISPR-Cas therapy

Background: In-vivo CRISPR Cas genome editing is a complex therapy involving lipid nanoparticle (LNP), messenger RNA (mRNA), and single guide RNA (sgRNA). This novel modality requires prior modeling to predict dose-exposure-response relationships due to limited information on sgRNA and mRNA biodistribution. This work presents a QSP model to characterize, predict, and translate the Pharmacokinetics/Pharmacodynamics (PK/PD) of CRISPR therapies from preclinical species (mouse, non-human primate (NHP)) to humans using two case studies: transthyretin amyloidosis and LDL-cholesterol reduction. Methods: PK/PD data were sourced from literature. The QSP model incorporates mechanisms post-IV injection: 1) LNP binding to opsonins in liver vasculature; 2) Phagocytosis into the Mononuclear Phagocytotic System (MPS); 3) LNP internalization via endocytosis and LDL receptor-mediated endocytosis in the liver; 4) Cellular internalization and transgene product release; 5) mRNA and sgRNA disposition via exocytosis and clathrin-mediated endocytosis; 6) Renal elimination of LNP and sgRNA; 7) Exonuclease degradation of sgRNA and mRNA; 8) mRNA translation into Cas9 and RNP complex formation for gene editing. Monte-Carlo simulations were performed for 1000 subjects and showed a reduction in serum TTR. Results: The rate of internalization in interstitial layer was 0.039 1/h in NHP and 0.007 1/h in humans. The rate of exocytosis was 6.84 1/h in mouse, 2690 1/h in NHP, and 775 1/h in humans. Pharmacodynamics were modeled using an indirect response model, estimating first-order degradation rate (0.493 1/d) and TTR reduction parameters in NHP. Discussion: The QSP model effectively characterized biodistribution and dose-exposure relationships, aiding the development of these novel therapies. The utility of platform QSP model can be paramount in facilitating the discovery and development of these novel agents.

Translational pharmacokinetic/pharmacodynamic model for mRNA-0184, an investigational therapeutic for the treatment of heart failure

Heart failure (HF) is a complex, progressive disorder that is associated with substantial morbidity and mortality on a global scale. Relaxin-2 is a naturally occurring hormone that may have potential therapeutic benefit for patients with HF. To investigate the therapeutic potential of relaxin in the treatment of patients with HF, mRNA-0184, a novel, investigational, lipid nanoparticle (LNP)-encapsulated mRNA therapy that encodes for human relaxin-2 fused to variable light chain kappa (Rel2-vlk) was developed. A translational semi-mechanistic population pharmacokinetic (PK)/pharmacodynamic (PD) model was developed using data from non-human primates at dose levels ranging from 0.15 to 1 mg/kg. The PK/PD model was able to describe the PK of Rel2-vlk mRNA and translated Rel2-vlk protein in non-human primates adequately with relatively precise estimates. The preclinical PK/PD model was then scaled allometrically to determine the human mRNA-0184 dose that would achieve therapeutic levels of Rel2-vlk protein expression in patients with stable HF with reduced ejection fraction. Model-based simulations derived from the scaled PK/PD model support the selection of 0.025 mg/kg as an appropriate starting human dose of mRNA-0184 to achieve average trough relaxin levels between 1 and 2.5 ng/mL, which is the potential exposure for cardioprotective action of relaxin.

Perspective from developers: Tissue-engineered products for skin wound healing

Tissue-engineered products (TEPs) are at the forefront of developmental medicines, precisely where monoclonal antibodies and recombinant cytokines were 30 years ago. TEPs development for treating skin wounds has become a fast-growing field as it offers the potential to find novel therapeutic approaches for treating pathologies that currently have limited or no effective alternatives. This review aims to provide the reader with the process of translating an idea from the laboratory bench to clinical practice, specifically in the context of TEPs designing for skin wound healing. It encompasses historical perspectives, approved therapies, and offers a distinctive insight into the regulatory framework in Brazil. We explore the essential guidelines for quality testing, and nonclinical proof-of-concept considering the Brazilian Network of Experts in Advanced Therapies (RENETA) and International Standards and Guidelines (ICH e ISO). Adopting a multifaceted approach, our discussion incorporates scientific and industrial perspectives, addressing quality, biosafety, non-clinical viability, clinical trial and real-word data for pharmacovigilance demands. This comprehensive analysis presents a panoramic view of the development of skin TEPs, offering insights into the evolving landscape of this dynamic and promising field.

Model-Based Virtual PK/PD Exploration and Machine Learning Approach to Define PK Drivers in Early Drug Discovery

While poor translatability of preclinical efficacy models can be responsible for clinical phase II failures, misdefinition of the optimal PK properties required to achieve therapeutic efficacy can also be a contributing factor. In the present work, the pharmacological dependency of PK end points in driving efficacy is demonstrated for six common pharmacological processes via model-based analysis. The analysis shows that the response is driven by multiple pharmacology-specific PK end points that change with how the response is defined. Moreover, the results demonstrate that the most important chemical structural features influencing response are specific to both target and downstream pharmacology, meaning the design and screening criteria must be defined uniquely for each target and corresponding pharmacology. The model-based virtual exploration of PK/PD relationships presented in this work offers one approach to identify target pharmacology-specific PK drivers and the associated potency-ADME space early in discovery to increase the probability of success and, ultimately, clinical attrition.

Translational Pharmacokinetic/Pharmacodynamic Model for mRNA-3927, an Investigational Therapeutic for the Treatment of Propionic Acidemia

Propionic acidemia (PA) is an ultrarare disorder caused by deficiency of the mitochondrial enzyme, propionyl-CoA carboxylase (PCC), composed of PCCA and PCCB subunits. An enzyme replacement therapy is being developed using dual messenger RNA (mRNA) therapy composed of lipid nanoparticles (LNPs) encapsulating mRNAs encoding PCCA and PCCB subunits of the PCC enzyme. We herein report on development of a translational semimechanistic pharmacokinetic (PK) and PK/pharmacodynamic (PD) model to quantify the relationship between the mRNA components of mRNA-3927 (an LNP encapsulating PCCA and PCCB mRNAs) and dose levels; PCCA/B mRNA PK and PD responses were assessed as circulating levels of primary disease markers 2-methyl citrate, 3-hydroxypropionate, and propionyl carnitine normalized to acetyl carnitine (C3/C2 ratio) to inform the first-in-human dose range and regimen selection. The translational PK/PD model was developed using preclinical data available in mice with PA, Sprague Dawley rats, and cynomolgus monkeys at dose levels ranging from 0.2 to 9 mg/kg. PCCA/B mRNA PK in mice, rats, and monkeys was adequately described using allometric scaling of volume and clearance parameters. The interspecies preclinical model was scaled allometrically to humans to predict the dose-response relationship in adult and pediatric patients with PA to guide selection of dose range and regimen for the Phase 1 clinical trial (ClinicalTrials.gov Identifier NCT04159103).

FOLFIRINOX Pharmacodynamic Interactions in 2D and 3D Pancreatic Cancer Cell Cultures

The multi-drug combination regime, FOLFIRINOX, is a standard of care chemotherapeutic therapy for pancreatic cancer patients. However, systematic evaluation of potential pharmacodynamic interactions among multi-drug therapy has not been reported previously. Here, pharmacodynamic interactions of the FOLFIRINOX agents (5-fluorouracil (5-FU), oxaliplatin (Oxa) and SN-38, the active metabolite of irinotecan) were assessed across a panel of primary and established pancreatic cancer cells. Inhibition of cell proliferation was quantified for each drug, alone and in combination, to obtain quantitative, drug-specific interaction parameters and assess the nature of drug interactions. The experimental data were analysed assuming Bliss independent interactions, and nonlinear regression model fitting was conducted in SAS. Estimates of the drug interaction term, psi (ψ), revealed that the Oxa/SN-38 combination appeared synergistic in PANC-1 (ψ = 0.6, 95% CI = 0.4, 0.9) and modestly synergistic, close to additive, in MIAPaCa-2 (ψ = 0.8, 95% CI = 0.6, 1.0) in 2D assays. The triple combination was strongly synergistic in MIAPaCa-2 (ψ = 0.2, 95% CI = 0.1, 0.3) and modestly synergistic/borderline additive in PANC-1 2D (ψ = 0.8, 95% CI = 0.6, 1.0). The triple combination showed antagonistic interactions in the primary PIN-127 and 3D PANC-1 model (ψ > 1). Quantitative pharmacodynamic interactions have not been described for the FOLFIRINOX regimen; this analysis suggests a complex interplay among the three chemotherapeutic agents. Extension of this pharmacodynamic analysis approach to clinical/translational studies of the FOLFIRINOX combination could reveal additional pharmacodynamic interactions and guide further refinement of this regimen to achieve optimal clinical responses.

Pharmacokinetic and Pharmacodynamic Modeling of siRNA Therapeutics - a Minireview

The approval of four small interfering RNA (siRNA) products in the past few years has demonstrated unequivocally the therapeutic potential of this novel modality. Three such products (givosiran, lumasiran and inclisiran) are liver-targeted, using tris N-acetylgalactosamine (GalNAc)₃ as the targeting ligand. Upon subcutaneous administration, GalNAc-conjugated siRNAs rapidly distribute into the liver via asialoglycoprotein receptor (ASGPR) mediated uptake in the hepatocytes, resulting in fast elimination from the systemic circulation. Patisiran, on the other hand, has been formulated in a lipid nanoparticle for optimal delivery to the liver. While several publications have described preclinical and clinical pharmacokinetic (PK) and pharmacodynamic (PD) results, including absorption, distribution, metabolism, and elimination (ADME) profiles in selected species as well as limited modeling efforts for siRNA therapeutics, there is no systematic review of the PK and PD models developed for these agents or work summarizing the utility and application(s) of such models in drug development and regulatory review. Here, we provide a mini-review of the current state of modeling efforts for siRNA therapeutics within the early preclinical, translational, and clinical stages of siRNA development. Diverse modeling methods including simple compartmental, mechanistic and systems PK/PD, physiologically-based PK (PBPK), population PK/PD, and dose-response-time models are introduced and reviewed. The utility of such models in development and regulatory review for siRNA therapeutics is also discussed with examples. Finally, the current knowledge gaps in mechanism of action of siRNA and resulting challenges in model development are summarized. The goal of this minireview is to trigger cross-functional discussion amongst all key stakeholders to generate key experimental datasets and align on current assumptions, model structures, and approaches to facilitate development and application of robust PK/PD models for siRNA therapeutics.

Quantitative pharmacokinetic analyses of anterior and posterior elimination routes of intravitreal anti-VEGF macromolecules using published human and rabbit data

The purpose of this study was to evaluate the contribution of the anterior elimination route for four anti-vascular endothelial growth factor (anti-VEGF) macromolecules (aflibercept, bevacizumab, pegaptanib and ranibizumab) after intravitreal injection using published human and rabbit data and three previously described pharmacokinetic (PK) modeling methods. A PubMed search was used to identify published studies with concentration-time data. The data were utilized only if the intravitreally injected drugs were used as plain solutions and several criteria for a well-performed PK study were fulfilled. The three methods to analyze rabbit data were (1) the equation for vitreal elimination half-life based molecular size assuming anterior elimination, (2) Maurice equation and plot for the ratio of aqueous humor (AH) to vitreal concentration assuming anterior elimination, and (3) the equation for amount of macromolecule eliminated anteriorly based on the area under the curve in AH. The first and third methods were used for human data. In the second and third methods, AH flow rate is a key model parameter, and it was varied between 2 and 3 μl/min. The methods were applied to data from 9 rabbit studies (1 for aflibercept, 5 for bevacizumab, and 3 for ranibizumab) and 5 human studies (1 for aflibercept, 3 for bevacizumab, and 1 for ranibizumab). Experimental half-lives of anti-VEGF macromolecules in both vitreous and aqueous humor were close to those calculated with the equations for vitreal elimination half-life in humans and rabbits. Rabbit data analyzed with Maurice plot indicated that the contribution of anterior elimination was usually at least 75%. In most human and rabbit studies, the calculated percentage of anterior elimination was at least 51%. Variability between studies was extensive for bevacizumab and ranibizumab. The results suggest that the anterior elimination route dominates after intravitreal injection of anti-VEGF macromolecules. However, the clinical data are sparse and variability is extensive, the latter emphasizing the need of proper experimental design.

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.

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.

Minimal Physiologically Based Pharmacokinetic-Pharmacodynamic (mPBPK-PD) Model of N-Acetylgalactosamine-Conjugated Small Interfering RNA Disposition and Gene Silencing in Preclinical Species and Humans

Conjugation of small interfering RNA (siRNA) to tris N-acetylgalactosamine [(GalNAc)₃] can enable highly selective, potent, and durable knockdown of targeted proteins in the liver. However, potential knowledge gaps between in vitro experiments, preclinical species, and clinical scenarios remain. A minimal physiologically based pharmacokinetic-pharmacodynamic model for GalNAc-conjugated siRNA (GalNAc-siRNA) was developed using published data for fitusiran (ALN-AT3), an investigational compound targeting liver antithrombin (AT), to delineate putative determinants governing the whole-body-to-cellular pharmacokinetic (PK) and pharmacodynamic (PD) properties of GalNAc-siRNA and facilitate preclinical-to-clinical translation. The model mathematically linked relevant mechanisms: 1) hepatic biodistribution, 2) tris-GalNAc binding to asialoglycoprotein receptors (ASGPRs) on hepatocytes, 3) ASGPR endocytosis and recycling, 4) endosomal transport and escape of siRNA, 5) cytoplasmic RNA-induced silencing complex (RISC) loading, 6) degradation of target mRNA by bound RISC, and 7) knockdown of protein. Physiologic values for 36 out of 48 model parameters were obtained from the literature. Kinetic parameters governing (GalNAc)₃-ASGPR binding and internalization were derived from published studies of uptake in hepatocytes. The proposed model well characterized reported pharmacokinetics, RISC dynamics, and knockdown of AT mRNA and protein by ALN-AT3 in mice. The model bridged multiple PK-PD data sets in preclinical species (mice, rat, monkey) and successfully captured reported plasma pharmacokinetics and AT knockdown in a phase I ascending-dose study. Estimates of in vivo potency were similar (∼2-fold) across species. Subcutaneous absorption and serum AT degradation rate constants scaled across species by body weight with allometric exponents of -0.29 and -0.22. The proposed mechanistic modeling framework characterizes the unique PK-PD properties of GalNAc-siRNA. SIGNIFICANCE STATEMENT: Tris N-acetylgalactosamine (GalNAc)₃-conjugated small interfering RNA (siRNA) therapeutics enable liver-targeted gene therapy and precision medicine. Using a translational and systems-based minimal physiologically based pharmacokinetic-pharmacodynamic (mPBPK-PD) modeling approach, putative determinants influencing GalNAc-conjugated siRNA (GalNAc-siRNA) functionality in three preclinical species and humans were investigated. The developed model successfully integrated and characterized relevant published in vitro-derived biomeasures, mechanistic PK-PD profiles in animals, and observed clinical PK-PD responses for an investigational GalNAc-siRNA (fitusiran). This modeling effort delineates the disposition and liver-targeted pharmacodynamics of GalNAc-siRNA.

In vivo-in vitro correlation of antitumor activity of heat shock protein 90 (HSP90) inhibitors with a pharmacokinetics/pharmacodynamics analysis using NCI-N87 xenograft mice

The in vitro antitumor activity (e.g. IC₅₀) of anticancer drugs is important for selecting candidate compounds for in vivo drug efficacy study in the early stage of drug discovery. In this study, we investigated the relationship between in vitro IC₅₀ and in vivo EC₅₀ using six heat shock protein 90 (HSP90) inhibitors.IC₅₀ of each compound was calculated from in vitro cell proliferation assays using the NCI-N87 cancer cell line. Each compound was administered to NCI-N87 xenograft mice, and EC₅₀ and the maximum tumour-killing rate constant were calculated from pharmacokinetics/pharmacodynamics analyses using plasma concentrations and tumour volumes.IC₅₀ obtained in vitro was poorly correlated with EC₅₀ obtained in vivo, while a good correlation (r = 0.856) was observed between them when corrected with the unbound fraction ratio.The results of this study using of HSP90 inhibitors as model compounds suggest importance of the consideration of an unbound fraction to evaluate the relationship between IC₅₀ and EC₅₀. These results will contribute to improvement in the prediction accuracy of in vivo drug efficacy from in vitro activity and the efficiency of drug discovery research.

Modeling approaches for reducing safety-related attrition in drug discovery and development: a review on myelotoxicity, immunotoxicity, cardiovascular toxicity, and liver toxicity

Introduction:Safety and tolerability is a critical area where improvements are needed to decrease the attrition rates during development of new drug candidates. Modeling approaches, when smartly implemented, can contribute to this aim.Areas covered:The focus of this review was on modeling approaches applied to four kinds of drug-induced toxicities: hematological, immunological, cardiovascular (CV) and liver toxicity. Papers, mainly published in the last 10 years, reporting models in three main methodological categories - computational models (e.g., quantitative structure-property relationships, machine learning approaches, neural networks, etc.), pharmacokinetic-pharmacodynamic (PK-PD) models, and quantitative system pharmacology (QSP) models - have been considered.Expert opinion:The picture observed in the four examined toxicity areas appears heterogeneous. Computational models are typically used in all areas as screening tools in the early stages of development for hematological, cardiovascular and liver toxicity, with accuracies in the range of 70-90%. A limited number of computational models, based on the analysis of drug protein sequence, was instead proposed for immunotoxicity. In the later stages of development, toxicities are quantitatively predicted with reasonably good accuracy using either semi-mechanistic PK-PD models (hematological and cardiovascular toxicity), or fully exploited QSP models (immuno-toxicity and liver toxicity).

Translational PK/PD and model-informed development of JNJ-67842125, a Fab reversal agent for JNJ-64179375, a long-acting thrombin inhibitor

BACKGROUND AND PURPOSE

Antigen-binding fragment (F_ab ) reversal agents were developed to reverse, in bleeding emergency, the long-acting anticoagulant effect of JNJ-64179375 (JNJ-9375), a monoclonal antibody that binds exosite-1 on thrombin.

EXPERIMENTAL APPROACH

The pharmacokinetic and pharmacodynamic (PK/PD) activities of three reversal agents of varying in vitro binding affinities to JNJ-9375 were characterised in cynomolgus monkeys. The time course of JNJ-9375 anticoagulant activity and reversal effects of each agent were evaluated. A mechanism-based PK/PD model, which integrated free serum concentrations of reversal agent, total and free serum concentrations of JNJ-9375, and thrombin time, was developed to quantitatively relate JNJ-9375 neutralisation to reversal of induced thrombin time prolongation. Model-based allometric scale-up of the lead reversal agent and the PK/PD relationship of JNJ-9375 in healthy volunteers were utilised to predict clinical dosing regimens.

KEY RESULTS

Lowering of free JNJ-9375 by the reversal agents corresponded with reversal of thrombin time prolongation. Total JNJ-9375 displayed typical mAb clearance at 2.75 ml·day^-1 ·kg^-1 , whereas reversal agents cleared faster between 1400 and 2400 ml·day^-1 ·kg^-1 . The model-estimated in vivo K_D values for JNJ-9375 reversal agents were 9 nM (ICHB-256), 0.4 nM (ICHB-281) and 13.7 pM (ICHB-164), in rank-ordered agreement of their K_D values determined in vitro. The three reversal agents exhibited different neutralisation characteristics in vivo, governed primarily by their binding kinetics to JNJ-9375. The model predicted a priori free JNJ-9375 kinetics after dosing ICHB-164 (JNJ-67842125) and JNJ-9375 under a different regimen.

CONCLUSION AND IMPLICATIONS

The results enabled selection of JNJ-67842125 as the reversal agent for JNJ-9375.

The Combination of Cell Cultured Technology and In Silico Model to Inform the Drug Development

Human-derived in vitro models can provide high-throughput efficacy and toxicity data without a species gap in drug development. Challenges are still encountered regarding the full utilisation of massive data in clinical settings. The lack of translated methods hinders the reliable prediction of clinical outcomes. Therefore, in this study, in silico models were proposed to tackle these obstacles from in vitro to in vivo translation, and the current major cell culture methods were introduced, such as human-induced pluripotent stem cells (hiPSCs), 3D cells, organoids, and microphysiological systems (MPS). Furthermore, the role and applications of several in silico models were summarised, including the physiologically based pharmacokinetic model (PBPK), pharmacokinetic/pharmacodynamic model (PK/PD), quantitative systems pharmacology model (QSP), and virtual clinical trials. These credible translation cases will provide templates for subsequent in vitro to in vivo translation. We believe that synergising high-quality in vitro data with existing models can better guide drug development and clinical use.

Phase 1 Clinical Trials of Small Molecules: Evolution and State of the Art

BACKGROUND

Phase 1 studies comprise the first exposure of a promising new chemical entity in healthy volunteers or, when appropriate, in patients. To assure a solid foundation for subsequent drug development, this first step must carefully assess the safety and tolerance of a new compound and often provide some indication of potential effect, so that a safe dose or dose range can be confidently selected for the initial Phase 2 efficacy study in the target patient population.

METHODS

This review was based on a literature search using both Google Scholar and PubMed, dated back to 1970, using search terms including "healthy volunteers", "Phase 1", and "normal volunteers", and also based on the authors' own experience conducting Phase 1 clinical trials. This paper reviews the history of Phase 1 studies of small molecules and their rapid evolution, focusing on the critical single and multiple dose studies, their designs, methodology, use of pharmacokinetic and pharmacodynamic modeling, application of potentially helpful biomarkers, study stopping criteria, and novel study designs.

RESULTS

We advocate for determining the safe dose range of a new compound by conducting careful dose escalation in a well-staffed inpatient setting, defining the maximally tolerated dose (MTD) by reaching the minimally intolerated dose (MID). The dose immediately below the MID is then defined as the MTD. This is best accomplished by using appropriately screened patients for the target indication, as patients in many CNS indications often tolerate doses differently than healthy non-patients. Biomarkers for safety and pharmacodynamic measures can also assist in further defining a safe and potentially effective dose range for subsequent clinical trial phases.

CONCLUSION

Phase 1 studies can yield critical insights into the pharmacology of a new compound in man and offer perhaps the only development period in which the dose range can be safely and thoroughly explored. Phase 1 studies often contain multiple endpoint objectives, the reconciliation of which can present a dilemma for drug developers and study investigators alike, but which can crucially determine whether a compound can survive to the next step in the drug development process.

Physiologically based pharmacokinetic (PBPK) modeling of RNAi therapeutics: Opportunities and challenges

Physiologically based pharmacokinetic (PBPK) modeling is a powerful tool with many demonstrated applications in various phases of drug development and regulatory review. RNA interference (RNAi)-based therapeutics are a class of drugs that have unique pharmacokinetic properties and mechanisms of action. With an increasing number of RNAi therapeutics in the pipeline and reaching the market, there is a considerable amount of active research in this area requiring a multidisciplinary approach. The application of PBPK models for RNAi therapeutics is in its infancy and its utility to facilitate the development of this new class of drugs is yet to be fully evaluated. From this perspective, we briefly discuss some of the current computational modeling approaches used in support of efficient development and approval of RNAi therapeutics. Considerations for PBPK model development are highlighted both in a relative context between small molecules and large molecules such as monoclonal antibodies and as it applies to RNAi therapeutics. In addition, the prospects for drawing upon other recognized avenues of PBPK modeling and some of the foreseeable challenges in PBPK model development for these chemical modalities are briefly discussed. Finally, an exploration of the potential application of PBPK model development for RNAi therapeutics is provided. We hope these preliminary thoughts will help initiate a dialogue between scientists in the relevant sectors to examine the value of PBPK modeling for RNAi therapeutics. Such evaluations could help standardize the practice in the future and support appropriate guidance development for strengthening the RNAi therapeutics development program.

Quantitative modeling predicts competitive advantages of a next generation anti-NKG2A monoclonal antibody over monalizumab for the treatment of cancer

A semimechanistic pharmacokinetic (PK)/receptor occupancy (RO) model was constructed to differentiate a next generation anti-NKG2A monoclonal antibody (KSQ mAb) from monalizumab, an immune checkpoint inhibitor in multiple clinical trials for the treatment of solid tumors. A three-compartment model incorporating drug PK, biodistribution, and NKG2A receptor interactions was parameterized using monalizumab PK, in vitro affinity measurements for both monalizumab and KSQ mAb, and receptor burden estimates from the literature. Following calibration against monalizumab PK data in patients with rheumatoid arthritis, the model successfully predicted the published PK and RO observed in gynecological tumors and in patients with squamous cell carcinoma of the head and neck. Simulations predicted that the KSQ mAb requires a 10-fold lower dose than monalizumab to achieve a similar RO over a 3-week period following q3w intravenous (i.v.) infusion dosing. A global sensitivity analysis of the model indicated that the drug-target binding affinity greatly affects the tumor RO and that an optimal affinity is needed to balance RO with enhanced drug clearance due to target mediated drug disposition. The model predicted that the KSQ mAb can be dosed over a less frequent regimen or at lower dose levels than the current monalizumab clinical dosing regimen of 10 mg/kg q2w. Either dosing strategy represents a competitive advantage over the current therapy. The results of this study demonstrate a key role for mechanistic modeling in identifying optimal drug parameters to inform and accelerate progression of mAb to clinical trials.

Epigenetics/Epigenomics and Prevention of Early Stages of Cancer by Isothiocyanates

Cancer is a complex disease and cancer development takes 10-50 years involving epigenetics. Evidence suggests that approximately 80% of human cancers are linked to environmental factors impinging upon genetics/epigenetics. Because advanced metastasized cancers are resistant to radiotherapy/chemotherapeutic drugs, cancer prevention by relatively nontoxic chemopreventive "epigenetic modifiers" involving epigenetics/epigenomics is logical. Isothiocyanates are relatively nontoxic at low nutritional and even higher pharmacologic doses, with good oral bioavailability, potent antioxidative stress/antiinflammatory activities, possess epigenetic-modifying properties, great anticancer efficacy in many in vitro cell culture and in vivo animal models. This review summarizes the latest advances on the role of epigenetics/epigenomics by isothiocyanates in prevention of skin, colon, lung, breast, and prostate cancers. The exact molecular mechanism how isothiocyanates modify the epigenetic/epigenomic machinery is unclear. We postulate "redox" processes would play important roles. In addition, isothiocyanates sulforaphane and phenethyl isothiocyanate, possess multifaceted molecular mechanisms would be considered as "general" cancer preventive agents not unlike chemotherapeutic agents like platinum-based or taxane-based drugs. Analogous to chemotherapeutic agents, the isothiocyanates would need to be used in combination with other nontoxic chemopreventive phytochemicals or drugs such as NSAIDs, 5-α-reductase/aromatase inhibitors targeting different signaling pathways would be logical for the prevention of progression of tumors to late advanced metastatic states.

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.

Translational prediction of first-in-human pharmacokinetics and pharmacodynamics of janagliflozin, a selective SGLT2 inhibitor, using allometric scaling, dedrick and PK/PD modeling methods

AIM

Janagliflozin is an orally selective SGLT2 inhibitor. To predict human pharmacokinetics/pharmacodynamics (PK/PD) characteristics of janagliflozin. To design optimal starting dose and effective dose for janagliflozin first-in-human (FIH) study.

METHODS

Animal PK/PD properties of janagliflozin were obtained from preclinical in vivo and in vitro study. Pharmacologically effective level of same class SGLT2 inhibitors were assessed through preclinical and clinical efficacy data of dapagliflozin, empagliflozin and canagliflozin. Human PK parameters and profiles of janagliflozin were predicted by various methods such as allometric scaling (AS), dedrick and PK/PD modeling analysis. Mechanistic PK/PD model was developed to describe janagliflozin-mediated impact on urinary glucose excretion (UGE). Human IC₅₀ was scaled from rat model-estimated IC₅₀ by correcting interspecies difference of in vitro IC₅₀ and plasma f_u of rat and human. The quantitative PK/PD prediction of janagliflozin was evaluated via observed PK/PD profiles of healthy subjects. Predicted PK/PD characteristics of janagliflozin were applied in FIH dose design. Optimal starting dose was suggested by considering preclinical PD and toxicity data of janagliflozin. Effective dose was suggested by considering pharmacologically effective level of same class drugs.

RESULTS

PK/PD characteristics of janagliflozin in preclinical species were summarized. Pharmacologically effective level for SGLT2 inhibitors was defined as 25~30% ΔUGE (ΔUGE=--(PG*GFR)_within24h) based on efficacy data of three same class drugs. Human predicted CL, V_ss and F were 1.04 L/h, 77.5 L and 0.80. Predicted AUC and C_max of janagliflozin of 10 and 50 mg were within 0.47~2.08 fold of observed values. Predicted human UGE_0-24 h and UGE_0-144 h of 10 and 50 mg dose range were within 0.66~1.41 fold of observed values. Optimal starting dose and pharmacologically active dose (PAD) were suggested as 10 mg and 50 mg. Dose range for FIH study was designed as 10-450 mg.

CONCLUSIONS

This study predicted human PK/PD characteristics of janagliflozin based on preclinical data and provide optimal dose design for janagliflozin FIH study based on pharmacologically effective level of same class drugs.

Pharmacodynamic Drug-Drug Interactions

Pharmacodynamic drug-drug interactions (DDIs) occur when the pharmacological effect of one drug is altered by that of another drug in a combination regimen. DDIs often are classified as synergistic, additive, or antagonistic in nature, albeit these terms are frequently misused. Within a complex pathophysiological system, the mechanism of interaction may occur at the same target or through alternate pathways. Quantitative evaluation of pharmacodynamic DDIs by employing modeling and simulation approaches is needed to identify and optimize safe and effective combination therapy regimens. This review investigates the opportunities and challenges in pharmacodynamic DDI studies and highlights examples of quantitative methods for evaluating pharmacodynamic DDIs, with a particular emphasis on the use of mechanism-based modeling and simulation in DDI studies. Advancements in both experimental and computational techniques will enable the application of better, model-informed assessments of pharmacodynamic DDIs in drug discovery, development, and therapeutics.

Relationship between mirtazapine dose and incidence of adrenergic side effects: An exploratory analysis

INTRODUCTION

Mirtazapine is an antidepressant with US Food and Drug Administration approval for management of major depressive disorder. Low doses of mirtazapine are often used for management of insomnia, with higher doses expected to provide more noradrenergic effect, and thus a higher degree of activation. If so, use of higher doses at bedtime may not be advisable and may worsen certain neuropsychiatric symptoms. No studies have been performed to evaluate these outcomes.

METHODS

This study consisted of a retrospective review of data submitted to the US Food and Drug Administration's Adverse Event Reporting System from January 1, 1995, to August 1, 2015. Cases that were deemed by study authors to represent activation of the noradrenergic system, and for which other confounders could not be identified, were included in the final analysis. The frequency of each specific adverse event was evaluated based on dose and compared to recent prescribing rates to determine if likelihood of a side effect increased with higher dose.

RESULTS

The study identified 308 incidences of anxiety, agitation, delusion, hallucination, hypertension, insomnia, nightmare, or tachycardia. After controlling for frequency of prescribing at a given dose, there was a statistically significant increase in rates of tachycardia which correlated with dose. However, after correction for multiple comparisons, results were no longer significant.

DISCUSSION

This study failed to support the hypothesis that mirtazapine is more activating at higher doses and appears to support the safety of increasing dose without increasing risk of noradrenergic side effects. Prospective studies will be necessary to confirm these findings.

Useful pharmacodynamic endpoints in children: selection, measurement, and next steps

Pharmacodynamic (PD) endpoints are essential for establishing the benefit-to-risk ratio for therapeutic interventions in children and neonates. This article discusses the selection of an appropriate measure of response, the PD endpoint, which is a critical methodological step in designing pediatric efficacy and safety studies. We provide an overview of existing guidance on the choice of PD endpoints in pediatric clinical research. We identified several considerations relevant to the selection and measurement of PD endpoints in pediatric clinical trials, including the use of biomarkers, modeling, compliance, scoring systems, and validated measurement tools. To be useful, PD endpoints in children need to be clinically relevant, responsive to both treatment and/or disease progression, reproducible, and reliable. In most pediatric disease areas, this requires significant validation efforts. We propose a minimal set of criteria for useful PD endpoint selection and measurement. We conclude that, given the current heterogeneity of pediatric PD endpoint definitions and measurements, both across and within defined disease areas, there is an acute need for internationally agreed, validated, and condition-specific pediatric PD endpoints that consider the needs of all stakeholders, including healthcare providers, policy makers, patients, and families.

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.

Integrated Pharmacokinetic/Pharmacodynamic Model of a Bispecific CD3xCD123 DART Molecule in Nonhuman Primates: Evaluation of Activity and Impact of Immunogenicity

Purpose: Flotetuzumab (MGD006 or S80880) is a bispecific molecule that recognizes CD3 and CD123 membrane proteins, redirecting T cells to kill CD123-expressing cells for the treatment of acute myeloid leukemia. In this study, we developed a mathematical model to characterize MGD006 exposure-response relationships and to assess the impact of its immunogenicity in cynomolgus monkeys.Experimental Design: Thirty-two animals received multiple escalating doses (100-300-600-1,000 ng/kg/day) via intravenous infusion continuously 4 days a week. The model reflects sequential binding of MGD006 to CD3 and CD123 receptors. Formation of the MGD006/CD3 complex was connected to total T cells undergoing trafficking, whereas the formation of the trimolecular complex results in T-cell activation and clonal expansion. Activated T cells were used to drive the peripheral depletion of CD123-positive cells. Anti-drug antibody development was linked to MGD006 disposition as an elimination pathway. Model validation was tested by predicting the activity of MGD006 in eight monkeys receiving continuous 7-day infusions.Results: MGD006 disposition and total T-cell and CD123-positive cell profiles were well characterized. Anti-drug antibody development led to the suppression of T-cell trafficking but did not systematically abolish CD123-positive cell depletion. Target cell depletion could persist after drug elimination owing to the self-proliferation of activated T cells generated during the first cycles. The model was externally validated with the 7-day infusion dosing schedule.Conclusions: A translational model was developed for MGD006 that features T-cell activation and expansion as a key driver of pharmacologic activity and provides a mechanistic quantitative platform to inform dosing strategies in ongoing clinical studies. Clin Cancer Res; 24(11); 2631-41. ©2018 AACR.

Protein Turnover Measurements in Human Serum by Serial Immunoaffinity LC-MS/MS

BACKGROUND

The half-life of target proteins is frequently an important parameter in mechanistic pharmacokinetic and pharmacodynamic (PK/PD) modeling of biotherapeutics. Clinical studies for accurate measurement of physiologically relevant protein turnover can reduce the uncertainty in PK/PD model-based predictions, for example, of the therapeutic dose and dosing regimen in first-in-human clinical trials.

METHODS

We used a targeted mass spectrometry work flow based on serial immunoaffinity enrichment ofmultiple human serum proteins from a [5,5,5-2H3]-L-leucine tracer pulse-chase study in healthy volunteers. To confirm the reproducibility of turnover measurements from serial immunoaffinity enrichment, multiple aliquots from the same sample set were subjected to protein turnover analysis in varying order. Tracer incorporation was measured by multiple–reaction-monitoring mass spectrometry and target turnover was calculated using a four-compartment pharmacokinetic model.

RESULTS

Five proteins of clinical or therapeutic relevance including soluble tumor necrosis factor receptor superfamily member 12A, tissue factor pathway inhibitor, soluble interleukin 1 receptor like 1, soluble mucosal addressin cell adhesion molecule 1, and muscle-specific creatine kinase were sequentially subjected to turnover analysis from the same human serum sample. Calculated half-lives ranged from 5–15 h; however, no tracer incorporation was observed for mucosal addressin cell adhesion molecule 1.

CONCLUSIONS

The utility of clinical pulse-chase studies to investigate protein turnover can be extended by serial immunoaffinity enrichment of target proteins. Turnover analysis from serum and subsequently from remaining supernatants provided analytical sensitivity and reproducibility for multiple human target proteins in the same sample set, irrespective of the order of analysis.

An allometric pharmacokinetic/pharmacodynamics model for BI 893923, a novel IGF-1 receptor inhibitor

PURPOSE

BI 893923 is a novel IGF1R/INSR inhibitor with promising anti-tumor efficacy. Dose-limiting hyperglycemia has been observed for other IGF1R/INSR inhibitors in clinical trials. To counterbalance anti-tumor efficacy with the risk of hyperglycemia and to determine the therapeutic window, we aimed to develop a translational pharmacokinetic/pharmacodynamics model for BI 893923. This aimed to translate pharmacokinetics and pharmacodynamics from animals to humans by an allometrically scaled semi-mechanistic model.

METHODS

Model development was based on a previously published PK/PD model for BI 893923 in mice (Titze et al., Cancer Chemother Pharmacol 77:1303-1314, 13). PK and blood glucose parameters were scaled by allometric principles using body weight as a scaling factor along with an estimation of the parameter exponents. Biomarker and tumor growth parameters were extrapolated from mouse to human using the body weight ratio as scaling factor.

RESULTS

The allometric PK/PD model successfully described BI 893923 pharmacokinetics and blood glucose across mouse, rat, dog, minipig, and monkey. BI 893923 human exposure as well as blood glucose and tumor growth were predicted and compared for different dosing scenarios. A comprehensive risk-benefit analysis was conducted by determining the net clinical benefit for each schedule. An oral dose of 2750 mg BI 893923 divided in three evenly distributed doses was identified as the optimal human dosing regimen, predicting a tumor growth inhibition of 90.4% without associated hyperglycemia.

CONCLUSION

Our model supported human therapeutic dose estimation by rationalizing the optimal efficacious dosing regimen with minimal undesired effects. This modeling approach may be useful for PK/PD scaling of other IGF1R/INSR inhibitors.

Enhancing value of clinical pharmacodynamics in oncology drug development: An alliance between quantitative pharmacology and translational science

Clinical pharmacodynamic evaluation is a key component of the "pharmacologic audit trail" in oncology drug development. We posit that its value can and should be greatly enhanced via application of a robust quantitative pharmacology framework informed by biologically mechanistic considerations. Herein, we illustrate examples of intersectional blindspots across the disciplines of quantitative pharmacology and translational science and offer a roadmap aimed at enhancing the caliber of clinical pharmacodynamic research in the development of oncology therapeutics.

Translational PK/PD modeling to increase probability of success in drug discovery and early development

In this review we present ways in which translational PK/PD modeling can address opportunities to enhance probability of success in drug discovery and early development. This is achieved by impacting efficacy and safety-driven attrition rates, through increased focus on the quantitative understanding and modeling of translational PK/PD. Application of the proposed principles early in the discovery and development phases is anticipated to bolster confidence of successfully evaluating proof of mechanism in humans and ultimately improve Phase II success. The present review is centered on the application of predictive modeling and simulation approaches during drug discovery and early development, and more specifically of mechanism-based PK/PD modeling. Case studies are presented, focused on the relevance of M&S contributions to real-world questions and the impact on decision making.

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

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

Translational mixed-effects PKPD modelling of recombinant human growth hormone - from hypophysectomized rat to patients

BACKGROUND AND PURPOSE

We aimed to develop a mechanistic mixed-effects pharmacokinetic (PK)-pharmacodynamic (PD) (PKPD) model for recombinant human growth hormone (rhGH) in hypophysectomized rats and to predict the human PKPD relationship.

EXPERIMENTAL APPROACH

A non-linear mixed-effects model was developed from experimental PKPD studies of rhGH and effects of long-term treatment as measured by insulin-like growth factor 1 (IGF-1) and bodyweight gain in rats. Modelled parameter values were scaled to human values using the allometric approach with fixed exponents for PKs and unscaled for PDs and validated through simulations relative to patient data.

KEY RESULTS

The final model described rhGH PK as a two compartmental model with parallel linear and non-linear elimination terms, parallel first-order absorption with a total s.c. bioavailability of 87% in rats. Induction of IGF-1 was described by an indirect response model with stimulation of kin and related to rhGH exposure through an Emax relationship. Increase in bodyweight was directly linked to individual concentrations of IGF-1 by a linear relation. The scaled model provided robust predictions of human systemic PK of rhGH, but exposure following s.c. administration was over predicted. After correction of the human s.c. absorption model, the induction model for IGF-1 well described the human PKPD data.

CONCLUSIONS

A translational mechanistic PKPD model for rhGH was successfully developed from experimental rat data. The model links a clinically relevant biomarker, IGF-1, to a primary clinical end-point, growth/bodyweight gain. Scaling of the model parameters provided robust predictions of the human PKPD in growth hormone-deficient patients including variability.

Quantitative prediction of human pharmacokinetics and pharmacodynamics of imigliptin, a novel DPP-4 inhibitor, using allometric scaling, IVIVE and PK/PD modeling methods

PURPOSE

To predict the pharmacokinetic/pharmacodynamic (PK/PD) profiles of imigliptin, a novel DPP-4 inhibitor, in first-in-human (FIH) study based on the data from preclinical species.

METHODS

Imigliptin was intravenously and orally administered to rats, dogs, and monkeys to assess their PK/PD properties. DPP-4 activity was the PD biomarker. PK/PD profiles of sitagliptin and alogliptin in rats and humans were obtained and digitized from literatures. PK/PD profiles of all dose levels for each drug in each species were analyzed using modeling approach. Human CL, Vss and PK profiles of imigliptin were then predicted using Allometric Scaling (AS), in vitro in vivo extrapolation (IVIVE), and the steady-state plasma drug concentration - mean residence time (Css-MRT) methods. In vitro EC50 corrected by fu and in vivo EC50 in rats corrected by interspecies difference of sitagliptin and alogliptin were utilized separately to predict imigliptin human EC50. The prediction by integrating all above methods was evaluated by comparing observed and simulated PK/PD profiles in healthy subjects.

RESULTS

Full PK/PD profiles in animal were summarized for imigliptin, sitagliptin and alogliptin. Imigliptin CL, Vss, and Fa were predicted to be 19.1L/h, 247L, and 0.81 in humans, respectively. Predicted imigliptin AUCs, AUECs, and Emax in humans were within 0.8-1.2 times of observed values whereas other predicted PK/PD parameters were within 0.5-1.5 times of observed values.

CONCLUSIONS

By integrating available preclinical and clinical data, FIH PK/PD profiles of imigliptin could be accurately predicted.

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.

Recent developments in using mechanistic cardiac modelling for drug safety evaluation

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Modelling and simulation can streamline decision making in drug safety testing.

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Computational cardiac electrophysiology is a mature technology with a long heritage.

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There are many challenges and opportunities in using in silico techniques in future.

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We discuss how models can be used at different stages of drug discovery.

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CiPA will combine screening platforms, human cell assays and in silico predictions.

On the tenth anniversary of two key International Conference on Harmonisation (ICH) guidelines relating to cardiac proarrhythmic safety, an initiative aims to consider the implementation of a new paradigm that combines in vitro and in silico technologies to improve risk assessment. The Comprehensive In Vitro Proarrhythmia Assay (CiPA) initiative (co-sponsored by the Cardiac Safety Research Consortium, Health and Environmental Sciences Institute, Safety Pharmacology Society and FDA) is a bold and welcome step in using computational tools for regulatory decision making. This review compares and contrasts the state-of-the-art tools from empirical to mechanistic models of cardiac electrophysiology, and how they can and should be used in combination with experimental tests for compound decision making.

Modeling and Simulation Tools: From Systems Biology to Systems Medicine

Modeling is an integral component of modern biology. In this chapter we look into the role of the model, as it pertains to Systems Medicine, and the software that is required to instantiate and run it. We do this by comparing the development, implementation, and characteristics of tools that have been developed to work with two divergent methodologies: Systems Biology and Pharmacometrics. From the Systems Biology perspective we consider the concept of "Software as a Medical Device" and what this may imply for the migration of research-oriented, simulation software into the domain of human health.In our second perspective, we see how in practice hundreds of computational tools already accompany drug discovery and development at every stage of the process. Standardized exchange formats are required to streamline the model exchange between tools, which would minimize translation errors and reduce the required time. With the emergence, almost 15 years ago, of the SBML standard, a large part of the domain of interest is already covered and models can be shared and passed from software to software without recoding them. Until recently the last stage of the process, the pharmacometric analysis used in clinical studies carried out on subject populations, lacked such an exchange medium. We describe a new emerging exchange format in Pharmacometrics which covers the non-linear mixed effects models, the standard statistical model type used in this area. By interfacing these two formats the entire domain can be covered by complementary standards and subsequently the according tools.

Deciphering the In Vivo Performance of a Monoclonal Antibody to Neutralize Its Soluble Target at the Site of Action in a Mouse Collagen-Induced Arthritis Model

PURPOSE

Study the disposition and target-neutralization capability of an anti-interleukin-6 (IL-6) monoclonal antibody (mAb) at the joint in a mouse collagen-induced arthritis (CIA) model.

METHODS

A mechanistic pharmacokinetic/pharmacodynamic study was conducted in a mouse CIA model using CNTO 345, a rat anti-mouse IL-6 mAb, as model compound. The drug, total/free IL-6 concentrations in both serum and joint lavage fluid were quantitatively assessed and compared to those in the normal control mice.

RESULTS

CNTO 345 exhibited higher clearance and significantly higher joint lavage/serum ratio in the CIA mice than in the normal control mice. The mAb concentrations in the joint lavage are approximately proportional to the serum concentrations at all the time points being examined. Dosing of CNTO 345 led to sustained free IL-6 suppression in both serum and joint lavage in a dose-dependent manner. A dose-dependent increase in total IL-6 was observed in serum, but not in the joint lavage fluid. Though no change in disease activity was observed following a single dose of anti-IL-6 mAb at peak of the disease, a dose-dependent decrease in serum amyloid A, a downstream biomarker of IL-6, was observed.

CONCLUSIONS

This study provided quantitative assessments of the distribution and target-neutralization capability of an anti-IL-6 mAb at the site of action in an animal disease model.

Utility of free and total target measurements as target engagement and efficacy biomarkers in biotherapeutic development--opportunities and challenges

For biotherapeutics directed against soluble targets, most often monoclonal antibodies (mAbs), their therapeutic efficacy theoretically is driven by the magnitude and duration of free target suppression. However, for soluble targets of rapid turnover and low abundance, it can be technically challenging to directly measure the lowering of free target following treatment with biologics. The opportunities, challenges, and practical approaches to assess free and bound soluble targets and the utility of free and bound target measurements as biomarkers for target engagement and efficacy are covered in this review. In particular, case examples are presented to illustrate the interplay between drug and free/bound target, and how an integrated bioanalytical and pharmacokinetic/target engagement/pharmacodynamic (PK/TE/PD) modeling approach can be used to assess the target engagement for biologics directed against soluble targets with rapid turnover. Important caveats of the modeling approach in the absence of free target measurements are also discussed.

Pharmacokinetics of Anti-VEGF Agent Aflibercept in Cancer Predicted by Data-Driven, Molecular-Detailed Model

Mathematical models can support the drug development process by predicting the pharmacokinetic (PK) properties of the drug and optimal dosing regimens. We have developed a pharmacokinetic model that includes a biochemical molecular interaction network linked to a whole-body compartment model. We applied the model to study the PK of the anti-vascular endothelial growth factor (VEGF) cancer therapeutic agent, aflibercept. Clinical data is used to infer model parameters using a Bayesian approach, enabling a quantitative estimation of the contributions of specific transport processes and molecular interactions of the drug that cannot be examined in other PK modeling, and insight into the mechanisms of aflibercept's antiangiogenic action. Additionally, we predict the plasma and tissue concentrations of unbound and VEGF-bound aflibercept. Thus, we present a computational framework that can serve as a valuable tool for drug development efforts.

Quantifying and Communicating Uncertainty in Preclinical Human Dose-Prediction

Human dose-prediction is fundamental for ranking lead-optimization compounds in drug discovery and to inform design of early clinical trials. This tutorial describes how uncertainty in such predictions can be quantified and efficiently communicated to facilitate decision-making. Using three drug-discovery case studies, we show how several uncertain pieces of input information can be integrated into one single uncomplicated plot with key predictions, including their uncertainties, for many compounds or for many scenarios, or both.

Modeling and Simulation Approaches for Cardiovascular Function and Their Role in Safety Assessment

Systems pharmacology modeling and pharmacokinetic-pharmacodynamic (PK/PD) analysis of drug-induced effects on cardiovascular (CV) function plays a crucial role in understanding the safety risk of new drugs. The aim of this review is to outline the current modeling and simulation (M&S) approaches to describe and translate drug-induced CV effects, with an emphasis on how this impacts drug safety assessment. Current limitations are highlighted and recommendations are made for future effort in this vital area of drug research.