Scaling the time-course of myelosuppression from rats to patients with a semi-physiological model

Translational PK/PD framework for antibody-drug conjugates to inform drug discovery and development

(171/200)ADCs represent a transformative class of medicine that combines the specificity of monoclonal antibodies with the potency of highly cytotoxic agents through linkers, aiming to enhance the therapeutic index of cytotoxic drugs. Given the complex molecular structures of ADCs, combining the molecular characteristics of small-molecule drugs and those of large-molecule biotherapeutics, there are several unique considerations when designing nonclinical-to-clinical PK/PD translation strategies.This complexity also demands a thorough understanding of the ADC's components-antibody, linker, and payload-to the overall toxicological, PK/PD, and efficacy profile. ADC development is a multidisciplinary endeavor requiring a strategic integration of nonclinical safety, pharmacology, and PK/PD modeling to translate from bench to bedside successfully.The ADC development underscores the necessity for a robust scientific foundation, leveraging advanced analytical and modeling tools to predict human responses and optimize therapeutic outcomes.This review aims to provide an ADC translational PK/PD framework by discussing unique aspects of ADC nonclinical to clinical PK translation, starting dose determination, and leveraging PK/PD modeling for human efficacious dose prediction and potential safety mitigation.

Model-based assessment of neutrophil-mediated phagocytosis and digestion of bacteria across in vitro and in vivo studies

Neutrophil granulocytes are key components of the host response against pathogens, and severe neutropenia, with neutrophil counts below 0.5 × 106 cells/mL, renders patients increasingly vulnerable to infections. Published in vitro (n = 7) and in vivo (n = 5) studies with time-course information on bacterial and neutrophil counts were digitized to characterize the kinetics of neutrophil-mediated bacterial killing and inform on the immune systems' contribution to the clearance of bacterial infections. A mathematical model for the in vitro dynamics of bacteria and the kinetics of neutrophil-mediated phagocytosis and digestion was developed, which was extended to in vivo studies in immune-competent and immune-compromised mice. Neutrophil-mediated bacterial killing was described by two first-order processes-phagocytosis and digestion-scaled by neutrophil concentration, where 50% of the maximum was achieved at neutrophil counts of 1.19 × 106 cells/mL (phagocytosis) and 6.55 × 106 cells/mL (digestion). The process efficiencies diminished as the phagocytosed bacteria to total neutrophils ratio increased (with 50% reduction at a ratio of 3.41). Neutrophil in vivo dynamics were captured through the characterization of myelosuppressive drug effects and postinoculation neutrophil influx into lungs and by system differences (27% bacterial growth and 9.3% maximum capacity, compared with in vitro estimates). Predictions showed how the therapeutically induced reduction of neutrophil counts enabled bacterial growth, especially when falling below 0.5 × 106 cells/mL, whereas control individuals could deal with all tested bacterial burdens (up to 109  colony forming units/g lung). The model-based characterization of neutrophil-mediated bacterial killing simultaneously predicted data across in vitro and in vivo studies and may be used to inform the capacity of host-response at the individual level.

The evolving role of investigative toxicology in the pharmaceutical industry

For decades, preclinical toxicology was essentially a descriptive discipline in which treatment-related effects were carefully reported and used as a basis to calculate safety margins for drug candidates. In recent years, however, technological advances have increasingly enabled researchers to gain insights into toxicity mechanisms, supporting greater understanding of species relevance and translatability to humans, prediction of safety events, mitigation of side effects and development of safety biomarkers. Consequently, investigative (or mechanistic) toxicology has been gaining momentum and is now a key capability in the pharmaceutical industry. Here, we provide an overview of the current status of the field using case studies and discuss the potential impact of ongoing technological developments, based on a survey of investigative toxicologists from 14 European-based medium-sized to large pharmaceutical companies.

A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development

Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E-R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E-R analysis in oncology clinical drug development are and what metrics of exposure should be considered.

Leveraging modeling and simulation to optimize the therapeutic window for epigenetic modifier drugs

Dysregulated epigenetic processes can lead to altered gene expression and give rise to malignant transformation and tumorigenesis. Epigenetic drugs aim to revert the phenotype of cancer cells to normally functioning cells, and are developed and applied to treat both hematological and solid cancers. Despite this promising therapeutic avenue, the successful development of epigenetic modulators has been challenging. We argue that besides identifying the right responder patient population, the selection of an optimized dosing regimen is equally important. For the majority of epigenetic modulators, hematological adverse effects such as thrombocytopenia, anemia or neutropenia are frequently observed and may limit their therapeutic potential. Therefore, one of the key challenges is to identify a dosing regimen that maximizes drug efficacy and minimizes toxicity. This requires a good understanding of the quantitative relationship between the administered dose, the drug exposure and the magnitude and duration of drug response related to safety and efficacy. With case examples, we highlight how modeling and simulation has been successfully applied to address those questions. As an outlook, we suggest the combination of efficacy and safety prediction models that capture the quantitative, mechanistic relationships governing the balance between their safety and efficacy dynamics. A stepwise approach for its implementation is presented. Utilizing in silico explorations, the impact of dosing regimen on the therapeutic window can be explored. This will serve as a basis to select the most promising dosing regimen that maximizes efficacy while minimizing adverse effects and to increase the probability of success for the given epigenetic drug.

Free Drug Theory - No Longer Just a Hypothesis?

The Free Drug Hypothesis is a well-established concept within the scientific lexicon pervading many areas of Drug Discovery and Development, and yet it is poorly defined by virtue of many variations appearing in the literature. Clearly, unbound drug is in dynamic equilibrium with respect to absorption, distribution, metabolism, elimination, and indeed, interaction with the desired pharmacological target. Binding interactions be they specific (e.g. high affinity) or nonspecific (e.g. lower affinity/higher capacity) are governed by the same fundamental physicochemical tenets including Hill-Langmuir Isotherms, the Law of Mass Action and Drug Receptor Theory. With this in mind, it is time to recognise a more coherent version and consider it the Free Drug Theory and a hypothesis no longer. Today, we have the experimental and modelling capabilities, pharmacological knowledge, and an improved understanding of unbound drug distribution (e.g. Kp_uu) to raise the bar on our understanding and analysis of experimental data. The burden of proof should be to rule out mechanistic possibilities and/or experimental error before jumping to the conclusion that any observations contradict these fundamentals.

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 approach from preclinical to clinical: comparison of dose finding methods of a new Bcl2 inhibitor using PK-PD modeling and interspecies extrapolation

The attrition rate of anticancer drugs during the clinical development remains very high. Interspecies extrapolation of anticancer drug pharmacodynamics (PD) could help to bridge the gap between preclinical and clinical settings and to improve drug development. Indeed, when combined with a physiologically-based-pharmacokinetics (PBPK) approach, PD interspecies extrapolation could be a powerful tool for predicting drug behavior in clinical trials. The present study aimed to explore this field for anticipating the clinical efficacy of a new Bcl-2 inhibitor, S 55746, for which dose ranging studies in xenografted mice and clinical data from a phase 1 trial involving cancer patients were available. Different strategies based on empirical or more mechanistic assumptions (based on PBPK-PD modelling) were developped and compared: the Rocchetti approach (ROC); the Orthogonal Rocchetti approach (oROC), a variant of ROC based on an orthogonal regression; the Consistent across species approach, bringing out an efficacy parameter assumed to be consistent across species; and the Scaling species-specific parameters approach, assuming the concentration-efficacy link is the same in mice as in humans, after allometric scaling. Empirical approaches (ROC and oROC) gave similar predictive performances and seemed to overestimate the active S 55746 dose compared to mechanistic approaches, while strategies elaborated from semi-mechanistic concepts and PBPK-PD modelling did not seem to be invalidated by clinical efficacy data. Also, empirical methods only predict a single dose level for the subsequent clinical studies, whereas mechanism-based strategies are more informative about the dose response relationship, highlighting the potential interest of such approaches in drug development.

Steering the Clinical Translation of Delivery Systems for Drugs and Health Products

Besides the feasibility for industrial scale-up, accelerating the translation from bench to bedside of new technological strategies for controlled delivery and targeting of drugs and other actives relevant for health management, such as medical devices and nutraceuticals, would benefit from an even earlier evaluation in pre-clinical models and clinical settings. At the same time, translational medicine also performs in the opposite direction, incorporating clinical needs and observations into scientific hypotheses and innovative technological proposals. With these aims, the sessions proposed for the 2019 CRS Italy Chapter Workshop will introduce the experience of Italian and worldwide researchers on how to foster the actual work in controlled release and drug delivery towards a reliable pre-clinical and clinical assessment.

Quantifying Drug-Induced Bone Marrow Toxicity Using a Novel Haematopoiesis Systems Pharmacology Model

Haematological toxicity associated with cancer therapeutics is monitored by changes in blood cell count, and their primary effect is on proliferative progenitors in the bone marrow. Using observations in rat bone marrow and blood, we characterize a mathematical model that comprises cell proliferation and differentiation of the full haematopoietic phylogeny, with interacting feedback loops between lineages in homeostasis as well as following carboplatin exposure. We accurately predicted the temporal dynamics of several mature cell types related to carboplatin-induced bone marrow toxicity and identified novel insights into haematopoiesis. Our model confirms a significant degree of plasticity within bone marrow cells, with the number and type of both early progenitors and circulating cells affecting cell balance, via feedback mechanisms, through fate decisions of the multipotent progenitors. We also demonstrated cross-species translation of our predictions to patients, applying the same core model structure and considering differences in drug-dependent and physiology-dependent parameters.

A Data Mining-Based Analysis of Medication Rules in Treating Bone Marrow Suppression by Kidney-Tonifying Method

Objective

To investigate the rule of kidney-tonifying method in Chinese medicine for the treatment of bone marrow suppression (BMS), in order to provide evidence and references for the clinical application of herbs and formulae.

Design

Collecting and sorting the information about the treatment of BMS related to kidney-tonifying (Bushen) method in Chinese medicine literatures on databases including Chinese National Knowledge Infrastructure (CNKI), and Chinese Biomedical Literature Database (CBM), establishing a database of BMS treating formulae after radiotherapy and chemotherapy with traditional Chinese medicine (TCM) kidney-tonifying method, and finally applying the relevant theories and techniques of data mining to analyze the medication rules of it.

Results

A total of 239 formulae and 202 herbs were included in this database, in which the herbs occurred 2,602 times in general. The high frequency herbs included Astragali Radix (Huangqi), Atractylodis Macrocephalae Rhizoma (Baizhu), and Ligustri Lucidi Fructus (Nvzhenzi). The main herb categories were deficiency-tonifying herbs, blood-activating herbs, dampness-draining diuretic herbs, heat-clearing herbs, and digestant herbs. Deficiency-tonifying herbs accounted for 64.60% of the total number. A total of 8 clustering formulae are summarized according to cluster analysis and 26 herb suits association rules are identified by Apriori algorithm.

Conclusion

The treatment of BMS is mainly based on the method of invigorating the spleen and tonifying the kidney and liver to strengthen healthy qi, supplementing with blood-activating herbs, and dampness-draining diuretic herbs to eliminate pathogenic factors.

Understanding Hematological Toxicities Using Mathematical Modeling

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

Quantitative translational modeling to facilitate preclinical to clinical efficacy & toxicity translation in oncology

Significant scientific advances in biomedical research have expanded our knowledge of the molecular basis of carcinogenesis, mechanisms of cancer growth, and the importance of the cancer immunity cycle. However, despite scientific advances in the understanding of cancer biology, the success rate of oncology drug development remains the lowest among all therapeutic areas. In this review, some of the key translational drug development objectives in oncology will be outlined. The literature evidence of how mathematical modeling could be used to build a unifying framework to answer these questions will be summarized with recommendations on the strategies for building such a mathematical framework to facilitate the prediction of clinical efficacy and toxicity of investigational antineoplastic agents. Together, the literature evidence suggests that a rigorous and unifying preclinical to clinical translational framework based on mathematical models is extremely valuable for making go/no-go decisions in preclinical development, and for planning early clinical studies.

In Silico Evaluation of Pharmacokinetic Optimization for Antimitogram-Based Clinical Trials

Antimitograms are prototype in vitro tests for evaluating chemotherapeutic efficacy using patient-derived primary cancer cells. These tests might help optimize treatment from a pharmacodynamic standpoint by guiding treatment selection. However, they are technically challenging and require refinements and trials to demonstrate benefit to be widely used. In this study, we performed simulations aimed at exploring how to validate antimitograms and how to complement them by pharmacokinetic optimization. A generic model of advanced cancer, including pharmacokinetic-pharmacodynamic monitoring, was used to link dosing schedules with progression-free survival (PFS), as built from previously validated modules. This model was used to explore different possible situations in terms of pharmacokinetic variability, pharmacodynamic variability, and antimitogram performance. The model recapitulated tumor dynamics and standalone therapeutic drug monitoring efficacy consistent with published clinical results. Simulations showed that combining pharmacokinetic and pharmacodynamic optimization should increase PFS in a synergistic fashion. Simulated data were then used to compute required clinical trial sizes, which were 30% to 90% smaller when pharmacokinetic optimization was added to pharmacodynamic optimization. This improvement was observed even when pharmacokinetic optimization alone exhibited only modest benefit. Overall, our work illustrates the synergy derived from combining antimitograms with therapeutic drug monitoring, permitting a disproportionate reduction of the trial size required to prove a benefit on PFS. Accordingly, we suggest that strategies with benefits too small for standalone clinical trials could be validated in combination in a similar manner.Significance: This work offers a method to reduce the number of patients needed for a clinical trial to prove the hypothesized benefit of a drug to progression-free survival, possibly easing opportunities to evaluate combinations. Cancer Res; 78(7); 1873-82. ©2018 AACR.

Pharmacokinetic-pharmacodynamic modelling of neutrophil response to G-CSF in healthy subjects and patients with chemotherapy-induced neutropenia

AIM

The objective of the present study was to use pharmacokinetic-pharmacodynamic modelling to characterize the effects of chemotherapy on the granulopoietic system and to predict the absolute neutrophil counts (ANCs) for patients with chemotherapy-induced neutropenia treated with filgrastim and pegfilgrastim.

METHODS

Data were extracted from 10 phase I-III studies conducted in 110 healthy adults, and 618 adult and 52 paediatric patients on chemotherapy following administration of filgrastim or pegfilgrastim. The structural model accounted for ANC dynamics and the effects of filgrastim and pegfilgrastim, chemotherapy and corticosteroids. The impact of neutrophils on drug disposition was based on a drug receptor-binding model that assumed quasi-equilibrium and stimulation of the production and maturation of neutrophils upon treatment. The chemotherapy and corticosteroid effects were represented by kinetic-pharmacodynamic-type models, where chemotherapy stimulated elimination of neutrophil precursors at the mitotic stage, and corticosteroids stimulated neutrophil production.

RESULTS

The systemic half-lives of filgrastim (2.6 h) and pegfilgrastim (10.1 h) were as expected. The effective half-life of chemotherapy was 9.6 h, with a 2-day killing effect. The rate of receptor elimination from mitotic compartments exhibited extreme interindividual variability (% coefficient of variation >200), suggesting marked differences in sensitivity to chemotherapy effects on ANCs. The stimulatory effects of pegfilgrastim were significantly greater than those of filgrastim. Model qualification confirmed the predictive capability of this model.

CONCLUSION

This qualified model simulates the time course of ANC in the absence or presence of chemotherapy and predicts nadir, time to nadir and time of recovery from different grades of neutropenia upon treatment with filgrastim and pegfilgrastim.

Generation of Methicillin-Resistant Staphylococcus Aureus Biofilm Infection in an Immunosuppressed Rat Model

Background

Methicillin-resistant Staphylococcus aureus (MRSA) is a common pathogen responsible for many related infections, and immunosuppressed individuals are more susceptible. Its pathogenicity is associated with its virulence factors, resistance to antibiotics, and ability to form biofilm (BF). MRSA-BF infections in immunosuppressed patients pose great difficulties to clinical treatment.

Material/Methods

The study aimed to establish a model of MRSA-BF infection in rats with cyclophosphamide (CTX)-induced immunosuppression. For this, rats were administered CTX on days 1 and 4. White blood cells (WBC) were counted, then rats were inoculated with a clinical MRSA 17546 (t037) on day 5. Rats were sacrificed on days 6–10 and tissue samples were examined by scanning electron microscopy.

Results

Using the dose of CTX: 150 (mg/kg) + 100 (mg/kg) is better than the other 2 programs as the survival rates of the immunocompromised rats were higher than in the other 2 immunosuppressive groups. The survival rate was not different between rats in the clean environment and in the SPF environment. However, the survival rate was affected by the sample acquisitions. Importantly, WBC counts started to decline on day 4, and then started to rise on day 9. Moreover, MRSA-BFs were formed earlier in immunosuppressed rats compared to the normal rats, as shown by scanning electron microscopy.

Conclusions

The study successfully established an immunosuppressed rat model of MRSA-BF infection, which provides methodological and data support for establishment of such animal models and is useful reference for related research. Our results may help further investigation of MRSA-BF infection.

Systems Pharmacology Model of Gastrointestinal Damage Predicts Species Differences and Optimizes Clinical Dosing Schedules

Gastrointestinal (GI) adverse events (AEs) are frequently dose limiting for oncology agents, requiring extensive clinical testing of alternative schedules to identify optimal dosing regimens. Here, we develop a translational mathematical model to predict these clinical AEs starting from preclinical GI toxicity data. The model structure incorporates known biology and includes stem cells, daughter cells, and enterocytes. Published data, including cellular numbers and division times, informed the system parameters for humans and rats. The drug‐specific parameters were informed with preclinical histopathology data from rats treated with irinotecan. The model fit the rodent irinotecan‐induced pathology changes well. The predicted time course of enterocyte loss in patients treated with weekly doses matched observed AE profiles. The model also correctly predicts a lower level of AEs for every 3 weeks (Q3W), as compared to the weekly schedule.

Translational Modeling of Drug-Induced Myelosuppression and Effect of Pretreatment Myelosuppression for AZD5153, a Selective BRD4 Inhibitor

In this work, we evaluate the potential risk of thrombocytopenia in man for a BRD4 inhibitor, AZD5153, based on the platelet count decreases from a Han Wistar rat study. The effects in rat were modeled and used to make clinical predictions for human populations with healthy baseline blood counts. At doses >10 mg, a dose-dependent effect on circulating platelets is expected, with similar predicted changes for both q.d. and b.i.d. dose schedules. These results suggest that at predicted efficacious doses, AZD5153 is likely to have some reductions in the clinical platelet counts, but within the normal range at projected efficacious doses. The model was then extended to incorporate preexisting myelosuppression where bone marrow function is inhibited by acute myeloid leukemia. Under these conditions, duration of platelet count recovery has the potential to be prolonged due to drug-induced myelosuppression.

Aurora kinase inhibitor nanoparticles target tumors with favorable therapeutic index in vivo

Efforts to apply nanotechnology in cancer have focused almost exclusively on the delivery of cytotoxic drugs to improve therapeutic index. There has been little consideration of molecularly targeted agents, in particular kinase inhibitors, which can also present considerable therapeutic index limitations. We describe the development of Accurin polymeric nanoparticles that encapsulate the clinical candidate AZD2811, an Aurora B kinase inhibitor, using an ion pairing approach. Accurins increase biodistribution to tumor sites and provide extended release of encapsulated drug payloads. AZD2811 nanoparticles containing pharmaceutically acceptable organic acids as ion pairing agents displayed continuous drug release for more than 1 week in vitro and a corresponding extended pharmacodynamic reduction of tumor phosphorylated histone H3 levels in vivo for up to 96 hours after a single administration. A specific AZD2811 nanoparticle formulation profile showed accumulation and retention in tumors with minimal impact on bone marrow pathology, and resulted in lower toxicity and increased efficacy in multiple tumor models at half the dose intensity of AZD1152, a water-soluble prodrug of AZD2811. These studies demonstrate that AZD2811 can be formulated in nanoparticles using ion pairing agents to give improved efficacy and tolerability in preclinical models with less frequent dosing. Accurins specifically, and nanotechnology in general, can increase the therapeutic index of molecularly targeted agents, including kinase inhibitors targeting cell cycle and oncogenic signal transduction pathways, which have to date proved toxic in humans.

Designing More Efficient Preclinical Experiments: A Simulation Study in Chemotherapy-Induced Myelosupression

A new more efficient preclinical study design (referred to as a compact design) is proposed that removes the need for satellite animals for the collection of toxicokinetic (TK) data by sampling from the main study animals, taking no more than one sample in 24 h to build up a full profile over the course of the study. The compact design’s performance was tested with a simulation study, using an example of chemotherapy-induced myelosupression in rats. Data sets were simulated from a model based on available data, following both the compact design and a traditional design using satellite animals, with 100 studies being simulated for each. The effect of the compact design on parameter and variance estimates for the TK and neutrophil models were investigated, as well as the potential effect of interoccasion variability (IOV). The compact design performed equally as well as the traditional design, and had little impact on parameter or variation estimates, indicating that it would be a suitable alternative to traditional satellite designs while reducing the number of animals required. When IOV was present but not accounted for during the TK analysis some parameter estimates were biased and interindividual variation and residual errors inflated; this was reduced by allowing for IOV in the analysis. Using the compact design removes the need for a satellite group, reducing the number of animals required, without affecting the ability to model the data. If large IOV is suspected, caution should be exercised to avoid parameter estimation bias, and inflation of variability and residual error.

Semimechanistic cell-cycle type-based pharmacokinetic/pharmacodynamic model of chemotherapy-induced neutropenic effects of diflomotecan under different dosing schedules

The current work integrates cell-cycle dynamics occurring in the bone marrow compartment as a key element in the structure of a semimechanistic pharmacokinetic/pharmacodynamic model for neutropenic effects, aiming to describe, with the same set of system- and drug-related parameters, longitudinal data of neutropenia gathered after the administration of the anticancer drug diflomotecan (9,10-difluoro-homocamptothecin) under different dosing schedules to patients (n = 111) with advanced solid tumors. To achieve such an objective, the general framework of the neutropenia models was expanded, including one additional physiologic process resembling cell cycle dynamics. The main assumptions of the proposed model are as follows: within the stem cell compartment, proliferative and quiescent cells coexist, and only cells in the proliferative condition are sensitive to drug effects and capable of following the maturation chain. Cell cycle dynamics were characterized by two new parameters, FProl (the fraction of proliferative [Prol] cells that enters into the maturation chain) and kcycle (first-order rate constant governing cell cycle dynamics within the stem cell compartment). Both model parameters were identifiable as indicated by the results from a bootstrap analysis, and their estimates were supported by date from the literature. The estimates of FProl and kcycle were 0.58 and 1.94 day(-1), respectively. The new model could properly describe the neutropenic effects of diflomotecan after very different dosing scenarios, and can be used to explore the potential impact of dosing schedule dependencies on neutropenia prediction.

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.

Optimizing oncology therapeutics through quantitative translational and clinical pharmacology: challenges and opportunities

Despite advances in biomedical research that have deepened our understanding of cancer hallmarks, resulting in the discovery and development of targeted therapies, the success rates of oncology drug development remain low. Opportunities remain for objective dose selection informed by exposure-response understanding to optimize the benefit-risk balance of novel therapies for cancer patients. This review article discusses the principles and applications of modeling and simulation approaches across the lifecycle of development of oncology therapeutics. Illustrative examples are used to convey the value gained from integration of quantitative clinical pharmacology strategies from the preclinical-translational phase through confirmatory clinical evaluation of efficacy and safety.

Dose schedule optimization and the pharmacokinetic driver of neutropenia

Toxicity often limits the utility of oncology drugs, and optimization of dose schedule represents one option for mitigation of this toxicity. Here we explore the schedule-dependency of neutropenia, a common dose-limiting toxicity. To this end, we analyze previously published mathematical models of neutropenia to identify a pharmacokinetic (PK) predictor of the neutrophil nadir, and confirm this PK predictor in an in vivo experimental system. Specifically, we find total AUC and Cmax are poor predictors of the neutrophil nadir, while a PK measure based on the moving average of the drug concentration correlates highly with neutropenia. Further, we confirm this PK parameter for its ability to predict neutropenia in vivo following treatment with different doses and schedules. This work represents an attempt at mechanistically deriving a fundamental understanding of the underlying pharmacokinetic drivers of neutropenia, and provides insights that can be leveraged in a translational setting during schedule selection.

Population pharmacokinetic-pharmacodynamic analysis for eribulin mesilate-associated neutropenia

AIMS

Eribulin mesilate is an inhibitor of microtubule dynamics that is approved for the treatment of late-stage metastatic breast cancer. Neutropenia is one of the major dose-limiting adverse effects of eribulin. The objective of this analysis was to develop a population pharmacokinetic-pharmacodynamic model for eribulin-associated neutropenia.

METHODS

A combined data set of 12 phase I, II and III studies for eribulin mesilate was analysed. The population pharmacokinetics of eribulin was described using a previously developed model. The relationship between eribulin pharmacokinetic and neutropenia was described using a semi-physiological lifespan model for haematological toxicity. Patient characteristics predictive of increased sensitivity to develop neutropenia were evaluated using a simulation framework.

RESULTS

Absolute neutrophil counts were available from 1579 patients. In the final covariate model, the baseline neutrophil count (ANC0) was estimated to be 4.03 × 10(9) neutrophils l(-1) [relative standard error (RSE) 1.2%], with interindividual variability (IIV, 37.3 coefficient of variation % [CV%]). The mean transition time was estimated to be 109 h (RSE 1.8%, IIV 13.9CV%), the feedback constant (γ) was estimated to be 0.216 (RSE 1.4%, IIV 12.2CV%), and the linear drug effect coefficient (SLOPE) was estimated to be 0.0451 μg l(-1) (RSE 3.2%, IIV 54CV%). Albumin, aspartate transaminase and receival of granulocyte colony-stimulating factor (G-CSF) were identified as significant covariates on SLOPE, and albumin, bilirubin, G-CSF, alkaline phosphatase and lactate dehydrogenase were identified as significant covariates on mean transition time.

CONCLUSIONS

The developed model can be applied to investigate optimal treatment strategies quantitatively across different patient groups with respect to neutropenia. Albumin was identified as the most clinically important covariate predictive of interindividual variability in the neutropenia time course.

Pharmacokinetic-pharmacodynamic modeling of antibacterial drugs

Pharmacokinetic-pharmacodynamic (PKPD) modeling and simulation has evolved as an important tool for rational drug development and drug use, where developed models characterize both the typical trends in the data and quantify the variability in relationships between dose, concentration, and desired effects and side effects. In parallel, rapid emergence of antibiotic-resistant bacteria imposes new challenges on modern health care. Models that can characterize bacterial growth, bacterial killing by antibiotics and immune system, and selection of resistance can provide valuable information on the interactions between antibiotics, bacteria, and host. Simulations from developed models allow for outcome predictions of untested scenarios, improved study designs, and optimized dosing regimens. Today, much quantitative information on antibiotic PKPD is thrown away by summarizing data into variables with limited possibilities for extrapolation to different dosing regimens and study populations. In vitro studies allow for flexible study designs and valuable information on time courses of antibiotic drug action. Such experiments have formed the basis for development of a variety of PKPD models that primarily differ in how antibiotic drug exposure induces amplification of resistant bacteria. The models have shown promise for efficacy predictions in patients, but few PKPD models describe time courses of antibiotic drug effects in animals and patients. We promote more extensive use of modeling and simulation to speed up development of new antibiotics and promising antibiotic drug combinations. This review summarizes the value of PKPD modeling and provides an overview of the characteristics of available PKPD models of antibiotics based on in vitro, animal, and patient data.

Predictions of in vivo prolactin levels from in vitro K(i) values of D(2) receptor antagonists using an agonist-antagonist interaction model

Prolactin elevation is a side effect of all currently available D(2) receptor antagonists used in the treatment of schizophrenia. Prolactin elevation is the result of a direct antagonistic D(2) effect blocking the tonic inhibition of prolactin release by dopamine. The aims of this work were to assess the correlation between in vitro estimates of D(2) receptor affinity and pharmacokinetic-pharmacodynamic model-based estimates obtained from analysis of clinical data using an agonist-antagonist interaction (AAI) model and to assess the value of such a correlation in early prediction of full prolactin time profiles. A population model describing longitudinal prolactin data was fitted to clinical data from 16 clinical phases 1 and 3 trials including five different compounds. Pharmacokinetic data were modeled for each compound and the prolactin model was both fitted in per-compound fits as well as simultaneously to all prolactin data. Estimates of prolactin elevating potency were compared to corresponding in vitro values and their predictability was evaluated through model-based simulations. The model successfully described the prolactin time course for all compounds. Estimates derived from experimental preclinical data and the model fit of the clinical data were strongly correlated (p<0.001), and simulations adequately predicted the prolactin elevation in five out of six compounds. The AAI model has the potential to be used in drug development to predict prolactin response for a given exposure of D(2) antagonists using routinely produced preclinical data.

Comparison of different semi-mechanistic models for chemotherapy-related neutropenia: application to BI 2536 a Plk-1 inhibitor

PURPOSE

The aim of this investigation was to compare the performance of a commonly used semi-mechanistic model for drug-related neutropenia with other semi-mechanistic models published in the literature.

METHODS

After their implementation in NONMEM VI, five semi-mechanistic models were assessed using the pharmacokinetic and absolute neutrophil count data obtained from 95 patients with non-small cell lung cancer receiving either 200 mg on day 1 or 50 or 60 mg on days 1, 2 and 3 of a 21-day treatment course with the new Plk-1 inhibitor BI 2536. The model performance was compared by means of predictive (visual and numerical) checks, precision in the parameter estimates and objective function-based measures. Details of model parameterization, model stability and run times are also provided.

RESULTS

The time course of the drug plasma concentrations was described by a three compartment model with a first-order elimination rate. With respect to neutropenia, all models were successfully implemented in NONMEM and provided reasonable fits for the median (although not all models described all percentiles of the data well), and in general precise parameter estimates.

CONCLUSION

In the current evaluation performed in a single drug, none of the models showed superior performance compared to the most commonly used model first described by Friberg et al. (J Clin Oncol 20:4713-4721, 2002).

Semi-mechanistic population pharmacokinetic/pharmacodynamic model for neutropenia following therapy with the Plk-1 inhibitor BI 2536 and its application in clinical development

PURPOSE

(1) To describe the neutropenic response of BI 2536 a polo-like kinase 1 inhibitor in patients with cancer using a semi-mechanistic model. (2) To explore by simulations (a) the neutropenic effects for the maximum tolerated dose (MTD) and the dose at which dose-limiting toxicity occurred, (b) the possibility to reduce the cycle duration without increasing neutropenia substantially, and (c) the impact of the initial absolute neutrophil count (ANC) on the degree of neutropenia for different doses.

EXPERIMENTAL DESIGN

BI 2536 was administered as intravenous infusion over 60 min in the dose range from 25 to 250 mg. Three different administration schedules were explored: (a) day 1, (b) days 1, 2, and 3 or (c) days 1 and 8 within a 3 week treatment cycle. BI 2536 plasma concentrations and ANC obtained during the first treatment cycle from 104 patients were analysed using the population approach with NONMEM VI.

RESULTS

Neutropenia was described by a semi-mechanistic model resembling proliferation at the stem cell compartment, maturation, degradation, and homeostatic regulation. BI 2536 acts decreasing proliferation rate. Simulations showed that (1) all MTD doses showed an acceptable risk of neutropenia, (2) when BI 2536 is given as 200 mg single administration, cycle duration can be reduced from 3 to 2 weeks, and (3) baseline ANC might be considered to individualise the dose of BI 2536.

CONCLUSIONS

A semi-mechanistic population model was applied to describe the neutropenic effects of BI 2536. The model was used for simulations to support further clinical development.

Predictive ability of a semi-mechanistic model for neutropenia in the development of novel anti-cancer agents: two case studies

In cancer chemotherapy neutropenia is a common dose-limiting toxicity. An ability to predict the neutropenic effects of cytotoxic agents based on proposed trial designs and models conditioned on previous studies would be valuable. The aim of this study was to evaluate the ability of a semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model for myelosuppression to predict the neutropenia observed in Phase I clinical studies, based on parameter estimates obtained from prior trials. Pharmacokinetic and neutropenia data from 5 clinical trials for diflomotecan and from 4 clinical trials for indisulam were used. Data were analyzed and simulations were performed using the population approach with NONMEM VI. Parameter sets were estimated under the following scenarios: (a) data from each trial independently, (b) pooled data from all clinical trials and (c) pooled data from trials performed before the tested trial. Model performance in each of the scenarios was evaluated by means of predictive (visual and numerical) checks. The semi-mechanistic PK/PD model for neutropenia showed adequate predictive ability for both anti-cancer agents. For diflomotecan, similar predictions were obtained for the three scenarios. For indisulam predictions were better when based on data from the specific study, however when the model parameters were conditioned on data from trials performed prior to a specific study, similar predictions of the drug related-neutropenia profiles and descriptors were obtained as when all data were used. This work provides further indication that modeling and simulation tools can be applied in the early stages of drug development to optimize future trials.