Supporting Prospective Pregnancy Trials via Modeling and Simulation: Lessons From the Past and Recommendations for the Future.

Despite the increasing awareness and guidance to support drug research and development in the pregnant population, there is still a high unmet medical need and off-label use in the pregnant population for mainstream, acute, chronic, rare disease, and vaccination/prophylactic use. There are many obstacles to enrolling the pregnant population in a study, ranging from ethical considerations, the complexity of the pregnancy stages, postpartum, fetus-mother interaction, and drug transfer to breast milk during lactation and impacts on neonates. This review will outline the common challenges of incorporating physiological differences in the pregnant population and historical but noninformative practice in a past clinical trial in pregnant women that led to labeling difficulties. The recommendations of different modeling approaches, such as a population pharmacokinetic model, physiologically based pharmacokinetic modeling, model-based meta-analysis, and quantitative system pharmacology modeling, are presented with some examples. Finally, we outline the gaps in the medical need for the pregnant population by classifying various types of diseases and some considerations that exist to support the use of medicines in this area. Ideas on the potential framework to support clinical trials and collaboration examples are also presented that could also accelerate understanding of drug research and medicine/prophylactics/vaccines in the pregnant population.

Moving Toward a Question-Centric Approach for Regulatory Decision Making in the Context of Drug Assessment.

The most intuitive question for market access for medicinal products is the benefit/risk (B/R) balance. The B/R assessment can conceptually be divided into subquestions related to establishing efficacy and safety. There are additional layers to the B/R ratio for medical products, including questions related to dose selection, clinical and nonclinical pharmacology, and drug quality. Explicitly stating the actual questions and how they contribute to the overall B/R provides a structure that fosters better informed cross-domain discussions. There is currently no systematic approach in the regulatory setting to assess and establish the acceptability of alternative methods and data sources. In most cases, the medicinal product sponsors tend to prioritize traditional data types and methods, which are well accepted by regulators for inclusion in regulatory submissions. This, in addition to the absence of rigor in the use and validation of new data types and methods, and the limited training of assessors in related fields can lead to increased regulatory skepticism toward new data types and methods. A data-knowledge backbone is needed to mitigate the uncertainty in efficacy and safety characterization. This white paper discusses the value of explicitly redefining and restructuring the regulatory scientific decision making around the scientific question to be addressed. The ecosystem proposed is based on three pillars: (i) a repository connecting questions, data, and methods; (ii) the development and validation of high-quality standards for data and methods; and (iii) credibility assessment. The ecosystem is applied to four use cases for illustration. The need for training and regulatory guidance is also discussed.

Use of pharmacodynamic modeling for Bayesian information borrowing in pediatric clinical trials.

The use of Bayesian methodology to design and analyze pediatric efficacy trials is one of the possible options to reduce their sample size. This reduction of the sample size results from the use of an informative prior for the parameters of interest. In most of the applications, the principle of 'information borrowing' from adults' trials is applied, which means that the informative prior is constructed using efficacy results in adult of the drug under investigation. This implicitly assumes similarity in efficacy between the selected pediatric dose and the efficacious dose in adults. The goal of this article is to propose a method to construct prior distribution for the parameter of interest, not directly constructed from the efficacy results of the efficacious dose in adult patients but using pharmacodynamic modeling of a bridging biomarker using early phase pediatric data. When combined with a model bridging the biomarker with the clinical endpoints, the prior is constructed using a variational method after simulation of the parameters of interest. A use case application illustrates how the method can be used to construct a realistic informative prior.

Pediatric oncology drug development and dosage optimization.

Oncology drug discovery and development has always been an area facing many challenges. Phase 1 oncology studies are typically small, open-label, sequential studies enrolling a small sample of adult patients (i.e., 3-6 patients/cohort) in dose escalation. Pediatric evaluations typically lag behind the adult development program. The pediatric starting dose is traditionally referenced on the recommended phase 2 dose in adults with the incorporation of body size scaling. The size of the study is also small and dependent upon the prevalence of the disease in the pediatric population. Similar to adult development, the dose is escalated or de-escalated until reaching the maximum tolerated dose (MTD) that also provides desired biological activities or efficacy. The escalation steps and identification of MTD are often rule-based and do not incorporate all the available information, such as pharmacokinetic (PK), pharmacodynamic (PD), tolerability and efficacy data. Therefore, it is doubtful if the MTD approach is optimal to determine the dosage. Hence, it is important to evaluate whether there is an optimal dosage below the MTD, especially considering the emerging complexity of combination therapies and the long-term tolerability and safety of the treatments. Identification of an optimal dosage is also vital not only for adult patients but for pediatric populations as well. Dosage-finding is much more challenging for pediatric populations due to the limited patient population and differences among the pediatric age range in terms of maturation and ontogeny that could impact PK. Many sponsors defer the pediatric strategy as they are often perplexed by the challenges presented by pediatric oncology drug development (model of action relevancy to pediatric population, budget, timeline and regulatory requirements). This leads to a limited number of approved drugs for pediatric oncology patients. This review article provides the current regulatory landscape, incentives and how they impact pediatric drug discovery and development. We also consider different pediatric cancers and potential clinical trial challenges/opportunities when designing pediatric clinical trials. An outline of how quantitative methods such as pharmacometrics/modelling & simulation can support the dosage-finding and justification is also included. Finally, we provide some reflections that we consider helpful to accelerate pediatric drug discovery and development.

Accelerating model-informed decisions for COVID-19 vaccine candidates using a model-based meta-analysis approach.

BACKGROUND: The COVID-19 pandemic has increased the need for innovative quantitative decision tools to support rapid development of safe and efficacious vaccines against SARS-CoV-2. To meet that need, we developed and applied a model-based meta-analysis (MBMA) approach integrating non-clinical and clinical immunogenicity and protection data. METHODS: A systematic literature review identified studies of vaccines against SARS-CoV-2 in rhesus macaques (RM) and humans. Summary-level data of 13 RM and 8 clinical trials were used in the analysis. A RM MBMA model was developed to quantify the relationship between serum neutralizing (SN) titres after vaccination and peak viral load (VL) post-challenge in RM. The translation of the RM MBMA model to a clinical protection model was then carried out to predict clinical efficacies based on RM data alone. Subsequently, clinical SN and efficacy data were integrated to develop three predictive models of efficacy - a calibrated RM MBMA, a joint (RM-Clinical) MBMA, and the clinical MBMA model. The three models were leveraged to predict efficacies of vaccine candidates not included in the model and efficacies against newer strains of SARS-CoV-2. FINDINGS: Clinical efficacies predicted based on RM data alone were in reasonable agreement with the reported data. The SN titre predicted to provide 50% efficacy was estimated to be about 21% of the mean human convalescent titre level, and that value was consistent across the three models. Clinical efficacies predicted from the MBMA models agreed with reported efficacies for two vaccine candidates (BBV152 and CoronaVac) not included in the modelling and for efficacies against delta variant. INTERPRETATION: The three MBMA models are predictive of protection against SARS-CoV-2 and provide a translational framework to enable early Go/No-Go and study design decisions using non-clinical and/or limited clinical immunogenicity data in the development of novel SARS-CoV-2 vaccines. FUNDING: This study was funded by Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA.

Cost effectiveness analyses of pharmacological treatments in heart failure.

In a rapidly growing and aging population, heart failure (HF) has become recognised as a public health concern that imposes high economic and societal costs worldwide. HF management stems from the use of highly cost-effective angiotensin converting enzyme inhibitors (ACEi) and ß-blockers to the use of newer drugs such as sodium-glucose cotransporter-2 inhibitors (SGLT2i), ivabradine, and vericiguat. Modelling studies of pharmacological treatments that report on cost effectiveness in HF is important in order to guide clinical decision making. Multiple cost-effectiveness analysis of dapagliflozin for heart failure with reduced ejection fraction (HFrEF) suggests that it is not only cost-effective and has the potential to improve long-term clinical outcomes, but is also likely to meet conventional cost-effectiveness thresholds in many countries. Similar promising results have also been shown for vericiguat while a cost effectiveness analysis (CEA) of empagliflozin has shown cost effectiveness in HF patients with Type 2 diabetes. Despite the recent FDA approval of dapagliflozin and empagliflozin in HF, it might take time for these SGLT2i to be widely used in real-world practice. A recent economic evaluation of vericiguat found it to be cost effective at a higher cost per QALY threshold than SGLT2i. However, there is a lack of clinical or real-world data regarding whether vericiguat would be prescribed on top of newer treatments or in lieu of them. Sacubitril/valsartan has been commonly compared to enalapril in cost effectiveness analysis and has been found to be similar to that of SGLT2i but was not considered a cost-effective treatment for heart failure with reduced ejection fraction in Thailand and Singapore with the current economic evaluation evidences. In order for more precise analysis on cost effectiveness analysis, it is necessary to take into account the income level of various countries as it is certainly easier to allocate more financial resources for the intervention, with greater effectiveness, in high- and middle-income countries than in low-income countries. This review aims to evaluate evidence and cost effectiveness studies in more recent HF drugs i.e., SGLT2i, ARNi, ivabradine, vericiguat and omecamtiv, and gaps in current literature on pharmacoeconomic studies in HF.

Epstein-Barr Virus Reactivation After Paediatric Haematopoietic Stem Cell Transplantation: Risk Factors and Sensitivity Analysis of Mathematical Model.

Epstein-Barr virus (EBV) establishes a lifelong latent infection in healthy humans, kept under immune control by cytotoxic T cells (CTLs). Following paediatric haematopoetic stem cell transplantation (HSCT), a loss of immune surveillance leads to opportunistic outgrowth of EBV-infected cells, resulting in EBV reactivation, which can ultimately progress to post-transplant lymphoproliferative disorder (PTLD). The aims of this study were to identify risk factors for EBV reactivation in children in the first 100 days post-HSCT and to assess the suitability of a previously reported mathematical model to mechanistically model EBV reactivation kinetics in this cohort. Retrospective electronic data were collected from 56 children who underwent HSCT at Great Ormond Street Hospital (GOSH) between 2005 and 2016. Using EBV viral load (VL) measurements from weekly quantitative PCR (qPCR) monitoring post-HSCT, a multivariable Cox proportional hazards (Cox-PH) model was developed to assess time to first EBV reactivation event in the first 100 days post-HSCT. Sensitivity analysis of a previously reported mathematical model was performed to identify key parameters affecting EBV VL. Cox-PH modelling revealed EBV seropositivity of the HSCT recipient and administration of anti-thymocyte globulin (ATG) pre-HSCT to be significantly associated with an increased risk of EBV reactivation in the first 100 days post-HSCT (adjusted hazard ratio (AHR) = 2.32, P = 0.02; AHR = 2.55, P = 0.04). Five parameters were found to affect EBV VL in sensitivity analysis of the previously reported mathematical model. In conclusion, we have assessed the effect of multiple covariates on EBV reactivation in the first 100 days post-HSCT in children and have identified key parameters in a previously reported mechanistic mathematical model that affect EBV VL. Future work will aim to fit this model to patient EBV VLs, develop the model to account for interindividual variability and model the effect of clinically relevant covariates such as rituximab therapy and ATG on EBV VL.

Concentration-QT modelling shows no evidence of clinically significant QT interval prolongation with capivasertib at expected therapeutic concentrations.

Pharmacokinetics-matched digital electrocardiogram data (n = 503 measurements from 180 patients) collected in a first-in-human, multi-part, dose-escalation (from 80 to 800 mg) and dose expansion (at 480 mg) phase 1 study in patients with advanced solid malignancies, were used to assess potential risk of QT prolongation associated with the AKT inhibitor capivasertib. The relationship between plasma drug concentrations and baseline-adjusted Fridericia-corrected QT (ΔQTcF) values was estimated using a prespecified linear mixed-effects model. The model provided an unbiased reproduction of the experimental data set, estimating a small but positive correlation between capivasertib concentration and ΔQTcF. At the expected therapeutic dose (400 mg twice daily) the predicted mean ΔQTcF at the steady state maximum concentration was 3.97 ms with an upper limit of the 90% CI of 5.07 ms; below the 10 ms limit proposed by ICH E14 guidance. This analysis suggests that capivasertib is not expected to present a clinically significant risk for QT prolongation that is associated with pro-arrhythmic effects.

Forward and reverse translational approaches to predict efficacy of neutralizing respiratory syncytial virus (RSV) antibody prophylaxis.

BACKGROUND: Neutralizing mAbs can prevent communicable viral diseases. MK-1654 is a respiratory syncytial virus (RSV) F glycoprotein neutralizing monoclonal antibody (mAb) under development to prevent RSV infection in infants. Development and validation of methods to predict efficacious doses of neutralizing antibodies across patient populations exposed to a time-varying force of infection (i.e., seasonal variation) are necessary. METHODS: Five decades of clinical trial literature were leveraged to build a model-based meta-analysis (MBMA) describing the relationship between RSV serum neutralizing activity (SNA) and clinical endpoints. The MBMA was validated by backward translation to animal challenge experiments and forward translation to predict results of a recent RSV mAb trial. MBMA predictions were evaluated against a human trial of 70 participants who received either placebo or one of four dose-levels of MK-1654 and were challenged with RSV [NCT04086472]. The MBMA was used to perform clinical trial simulations and predict efficacy of MK-1654 in the infant target population. FINDINGS: The MBMA established a quantitative relationship between RSV SNA and clinical endpoints. This relationship was quantitatively consistent with animal model challenge experiments and results of a recently published clinical trial. Additionally, SNA elicited by increasing doses of MK-1654 in humans reduced RSV symptomatic infection rates with a quantitative relationship that approximated the MBMA. The MBMA indicated a high probability that a single dose of ≥ 75 mg of MK-1654 will result in prophylactic efficacy (> 75% for 5 months) in infants. INTERPRETATION: An MBMA approach can predict efficacy of neutralizing antibodies against RSV and potentially other respiratory pathogens.

V2 ACHER: Visualization of complex trial data in pharmacometric analyses with covariates.

Pharmacometric models can enhance clinical decision making, with covariates exposing potential contributions to variability of subpopulation characteristics, for example, demographics or disease status. Intuitive visualization of models with multiple covariates is needed because sparsity of data in visualizations trellised by covariate values can raise concerns about the credibility of the underlying model. V2 ACHER, introduced here, is a stepwise transformation of data that can be applied to a variety of static (non-ordinary-differential-equation-based) pharmacometric analyses. This work uses four examples of increasing complexity to show how the transformation elucidates the relationship between observations and model results and how it can also be used in visual predictive checks to confirm the quality of a model. V2 ACHER facilitates consistent, intuitive, single-plot visualization of a multicovariate model with a complex data set, thereby enabling easier model communication for modelers and for cross-functional development teams and facilitating confident use in support of decisions.

Ceralasertib (AZD6738), an Oral ATR Kinase Inhibitor, in Combination with Carboplatin in Patients with Advanced Solid Tumors: A Phase I Study.

PURPOSE: This study reports the safety, tolerability, MTD, recommended phase II dose (RP2D), pharmacokinetic/pharmacodynamic profile, and preliminary antitumor activity of ceralasertib combined with carboplatin in patients with advanced solid tumors. It also examined exploratory predictive and pharmacodynamic biomarkers. PATIENTS AND METHODS: Eligible patients (n = 36) received a fixed dose of carboplatin (AUC5) with escalating doses of ceralasertib (20 mg twice daily to 60 mg once daily) in 21-day cycles. Sequential and concurrent combination dosing schedules were assessed. RESULTS: Two ceralasertib MTD dose schedules, 20 mg twice daily on days 4-13 and 40 mg once daily on days 1-2, were tolerated with carboplatin AUC5; the latter was declared the RP2D. The most common treatment-emergent adverse events (Common Terminology Criteria for Adverse Events grade ≥3) were anemia (39%), thrombocytopenia (36%), and neutropenia (25%). Dose-limiting toxicities of grade 4 thrombocytopenia (n = 2; including one grade 4 platelet count decreased) and a combination of grade 4 thrombocytopenia and grade 3 neutropenia occurred in 3 patients. Ceralasertib was quickly absorbed (tmax ∼1 hour), with a terminal plasma half-life of 8-11 hours. Upregulation of pRAD50, indicative of ataxia telangiectasia mutated (ATM) activation, was observed in tumor biopsies during ceralasertib treatment. Two patients with absent or low ATM or SLFN11 protein expression achieved confirmed RECIST v1.1 partial responses. Eighteen of 34 (53%) response-evaluable patients had RECIST v1.1 stable disease. CONCLUSIONS: The RP2D for ceralasertib plus carboplatin was established as ceralasertib 40 mg once daily on days 1-2 administered with carboplatin AUC5 every 3 weeks, with pharmacokinetic and pharmacodynamic studies confirming pharmacodynamic modulation and preliminary evidence of antitumor activity observed.

A meta-analysis of tumour response and relapse kinetics based on 34,881 patients: A question of cancer type, treatment and line of treatment.

PURPOSE: Cancer disease burden is commonly assessed radiologically in solid tumours in support of response assessment via the RECIST criteria. These longitudinal data are amenable to mathematical modelling and these models characterise the initial tumour size, initial tumour shrinkage in responding patients and rate of regrowth as patient's disease progresses. Knowing how these parameters vary between patient populations and treatments would inform translational modelling approaches from non-clinical data as well as clinical trial design. EXPERIMENTAL DESIGN: Here a meta-analysis of reported model parameter values is reported. Appropriate literature was identified via a PubMed search and the application of text-based clustering approaches. The resulting parameter estimates are examined graphically and with ANOVA. RESULTS: Parameter values from a total of 80 treatment arms were identified based on 80 trial arms containing a total of 34,881 patients. Parameter estimates are generally consistent. It is found that a significant proportion of the variation in rates of tumour shrinkage and regrowth are explained by differing cancer and treatment: cancer type accounts for 66% of the variation in shrinkage rate and 71% of the variation in reported regrowth rates. Mean average parameter values by cancer and treatment are also reported. CONCLUSIONS: Mathematical modelling of longitudinal data is most often reported on a per clinical trial basis. However, the results reported here suggest that a more integrative approach would benefit the development of new treatments as well as the further optimisation of those currently used.

Harnessing formulation and clinical pharmacology knowledge for efficient pediatric drug development: Overview and discussions from M-CERSI pediatric formulation workshop 2019.

A pediatric formulation workshop entitled "Pediatric Formulations: Challenges of Today and Strategies for Tomorrow" was held to advance pediatric drug product development efforts in both pre-competitive and competitive environments. The workshop had four main sessions discussing key considerations of Formulation, Analytical, Clinical and Regulatory. This paper focuses on the clinical session of the workshop. It provides an overview of the discussion on the interconnection of pediatric formulation design and development, clinical development strategy and pediatric clinical pharmacology. The success of pediatric drug product development requires collaboration of multi-disciplinary teams across the pharmaceutical industry, consortiums, foundations, academia and global regulatory agencies. Early strategic planning is essential to ensure alignment among major stakeholders of different functional teams. Such an alignment is particularly critical in the collaboration between formulators and clinical pharmacology teams.

Correction to: How is the Pharmaceutical Industry Structured to Optimize Pediatric Drug Development? Existing Pediatric Structure Models and Proposed Recommendations for Structural Enhancement.

The article How is the Pharmaceutical Industry Structured to Optimize Pediatric Drug Development? Existing Pediatric Structure Models and Proposed Recommendations for Structural Enhancement, written by Thomas Severin et al. was originally published electronically on the publisher's internet portal on February 6, 2020 without open access. With the author(s)' decision to opt for Open Choice the copyright of the article changed on April 22, 2020 to © The Author(s) 2020 and the article is forthwith distributed under a Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Artificial Intelligence and Machine Learning Applied at the Point of Care.

INTRODUCTION: The increasing availability of healthcare data and rapid development of big data analytic methods has opened new avenues for use of Artificial Intelligence (AI)- and Machine Learning (ML)-based technology in medical practice. However, applications at the point of care are still scarce. OBJECTIVE: Review and discuss case studies to understand current capabilities for applying AI/ML in the healthcare setting, and regulatory requirements in the US, Europe and China. METHODS: A targeted narrative literature review of AI/ML based digital tools was performed. Scientific publications (identified in PubMed) and grey literature (identified on the websites of regulatory agencies) were reviewed and analyzed. RESULTS: From the regulatory perspective, AI/ML-based solutions can be considered medical devices (i.e., Software as Medical Device, SaMD). A case series of SaMD is presented. First, tools for monitoring and remote management of chronic diseases are presented. Second, imaging applications for diagnostic support are discussed. Finally, clinical decision support tools to facilitate the choice of treatment and precision dosing are reviewed. While tested and validated algorithms for precision dosing exist, their implementation at the point of care is limited, and their regulatory and commercialization pathway is not clear. Regulatory requirements depend on the level of risk associated with the use of the device in medical practice, and can be classified into administrative (manufacturing and quality control), software-related (design, specification, hazard analysis, architecture, traceability, software risk analysis, cybersecurity, etc.), clinical evidence (including patient perspectives in some cases), non-clinical evidence (dosing validation and biocompatibility/toxicology) and other, such as e.g. benefit-to-risk determination, risk assessment and mitigation. There generally is an alignment between the US and Europe. China additionally requires that the clinical evidence is applicable to the Chinese population and recommends that a third-party central laboratory evaluates the clinical trial results. CONCLUSIONS: The number of promising AI/ML-based technologies is increasing, but few have been implemented widely at the point of care. The need for external validation, implementation logistics, and data exchange and privacy remain the main obstacles.

How is the Pharmaceutical Industry Structured to Optimize Pediatric Drug Development? Existing Pediatric Structure Models and Proposed Recommendations for Structural Enhancement.

BACKGROUND: Pediatric regulations enacted in both Europe and the USA have disrupted the pharmaceutical industry, challenging business and drug development processes, and organizational structures. Over the last decade, with science and innovation evolving, industry has moved from a reactive to a proactive mode, investing in building appropriate structures and capabilities as part of their business strategy to better tackle the challenges and opportunities of pediatric drug development. METHODS: The EFGCP Children's Medicines Working Party and the IQ Pediatric working group have joined their efforts to survey their member company representatives to understand how pharmaceutical companies are organized to fulfill their regulatory obligations and optimize their pediatric drug development programs. RESULTS: Key success factors and recommendations for a fit-for-purpose Pediatric Expert Group (PEG) were identified. CONCLUSION: Pediatric structures and expert groups were shown to be important to support optimization of the development of pediatric medicines.

Proliferation and AKT Activity Biomarker Analyses after Capivasertib (AZD5363) Treatment of Patients with ER+ Invasive Breast Cancer (STAKT).

PURPOSE: The STAKT study examined short-term exposure (4.5 days) to the oral selective pan-AKT inhibitor capivasertib (AZD5363) to determine if this drug can reach its therapeutic target in sufficient concentration to significantly modulate key biomarkers of the AKT pathway and tumor proliferation. PATIENTS AND METHODS: STAKT was a two-stage, double-blind, randomized, placebo-controlled, "window-of-opportunity" study in patients with newly diagnosed ER+ invasive breast cancer. Stage 1 assessed capivasertib 480 mg b.i.d. (recommended monotherapy dose) and placebo, and stage 2 assessed capivasertib 360 and 240 mg b.i.d. Primary endpoints were changes from baseline in AKT pathway markers pPRAS40, pGSK3ß, and proliferation protein Ki67. Pharmacologic and pharmacodynamic properties were analyzed from blood sampling, and tolerability by adverse-event monitoring. RESULTS: After 4.5 days' exposure, capivasertib 480 mg b.i.d. (n = 17) produced significant decreases from baseline versus placebo (n = 11) in pGSK3ß (H-score absolute change: -55.3, P = 0.006) and pPRAS40 (-83.8, P < 0.0001), and a decrease in Ki67 (absolute change in percentage positive nuclei: -9.6%, P = 0.031). Significant changes also occurred in secondary signaling biomarker pS6 (-42.3, P = 0.004), while pAKT (and nuclear FOXO3a) also increased in accordance with capivasertib's mechanism (pAKT: 81.3, P = 0.005). At doses of 360 mg b.i.d. (n = 5) and 240 mg b.i.d. (n = 6), changes in primary and secondary biomarkers were also observed, albeit of smaller magnitude. Biomarker modulation was dose and concentration dependent, and no new safety signals were evident. CONCLUSIONS: Capivasertib 480 mg b.i.d. rapidly modulates key biomarkers of the AKT pathway and decreases proliferation marker Ki67, suggesting future potential as an effective therapy in AKT-dependent breast cancers.

Industry Perspective on Using MIDD for Pediatric Studies Requiring Integration of Ontogeny.

Joining the Food and Drug Administration/University of Maryland Center of Excellence in Regulatory Science and Innovation Workshop to discuss and identify solutions to optimize pediatric drug development and, in particular, to address the question as to whether we are ready to incorporate pediatric ontogeny into modeling was the opportunity to share learnings, confront ideas, and present examples of studies performed in industry and academia. This was not only the opportunity to reflect on the experience and the knowledge so far within the current regulatory framework but also to look at the future and explore new and future approaches as well as best practices with the use of modeling and simulation and extrapolation as part of pediatric development.

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.