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.

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.

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.

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 β-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.

1010. Cross-Species Translation of Correlates of Protection for COVID-19 Vaccine Candidates Using Quantitative Tools

Background

Several COVID-19 vaccines have been authorized, and the need for rapid, further modification is anticipated. This work uses a Model-Based Meta-Analysis (MBMA) to relate, across species, immunogenicity to peak viral load (VL) after challenge and to clinical efficacy. Together with non-clinical and/or early clinical immunogenicity data (ECID), this enables prediction of a candidate vaccine’s clinical efficacy. The goal of this work was to enable the accelerated development of vaccine candidates by supporting Go/No-Go and study design decisions, and the resulting MBMA can be instrumental in decisions not to progress candidates to late stage development.

Methods

A literature review with pre-specified inclusion/exclusion criteria enabled creation of a database including nonclinical serum neutralizing titers (SN), peak VL after challenge with SARS-CoV-2 (VL), along with data from several clinical vaccine candidates. Rhesus Macaque (RM) and golden hamster (GH) were selected (due to availability and consistency of data) for MBMA modeling. For both RM and GH, peak post-challenge VL in lung and nasal tissues were used as surrogates for clinical disease and were related to pre-challenge SN via the MBMA. The VL predictions from the RM MBMA were scaled to incidence rates in humans, with a scaling factor between RM and human SN estimated using early Phase 3 efficacy data. This enabled clinical efficacy predictions based on ECID. To qualify the model’s predictive power, efficacies of COVID-19 vaccine candidates were compared to those predicted from the MBMA and their respective Ph1/2 SN data. More recently available clinical data enable building a clinical MBMA; comparing this to the RM MBMA further supports SN as predictive.

Results

The MBMA analyses identified a sigmoidal decrease in VL (increasing protection) with increase in SN in all three species, with more SN needed (in both RM and GH) for protection in nasal swabs than in BAL (see figure). The comparison between predicted and reported clinical efficacies demonstrated the model’s predictive power across vaccine platforms.

RM and GH MBMA Protection Models and Translational Prediction with Observed Efficacies

Sizes of circles indicate relative weight of the data in the respective quantitative model. Model and data visualizations have been harmonized (across tissue-types) separately for each of RM and GH using VACHER (Lommerse, et al., CPT:PSP, in press).

Conclusion

By quantifying adjustments needed between species and assays, translational MBMA can inform development decisions by using nonclinical SN and VL, and ECID to predict protection from COVID-19.

Disclosures

Anna Largajolli, PhD, Certara (Employee) Nele Plock, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Bhargava Kandala, PhD, Merck & Co., Inc. (Employee, Shareholder) Akshita Chawla, PhD, Merck & Co., Inc. (Employee, Shareholder) Seth H. Robey, PhD, Merck & Co., Inc. (Employee, Shareholder) Kenny Watson, PhD, Certara (Employee, Shareholder) Raj Thatavarti, MS, Certara (Employee, Shareholder) Sheri Dubey, PhD, Merck & Co., Inc. (Employee, Shareholder) S. Y. Amy Cheung, PhD, Certara (Employee, Shareholder) Rik de Greef, MSc, Certara (Employee, Shareholder) Jeffrey R. Sachs, PhD, Merck & Co., Inc. (Employee, Shareholder)

1013. Predicting RSV Efficacy for MK-1654 in Temperate and Tropical Climates using MBMA and Clinical Trial Simulation to Account for Seasonal Differences in RSV Force-of-Infection

Background

MK-1654 is a respiratory syncytial virus (RSV) F glycoprotein neutralizing monoclonal antibody under development to prevent RSV infection in infants. A model-based meta-analysis (MBMA) describing the relationship between RSV serum neutralizing activity (SNA) and clinically relevant endpoints (e.g. incidence rates) in humans, including lower respiratory tract infection (LRI) in infants, was presented previously. This model accounted for variable exposure to RSV over the course of the season through a force-of-infection (FOI) function modulating the overall risk of RSV infection over time. The objective of the current work was to determine whether variations in regional seasonality would impact the efficacy of a clinical trial evaluating MK-1654.

Methods

A FOI function to describe the degree of RSV exposure as a function of time was created by fitting epidemiological data to a Gaussian function added to a constant baseline value. Clinical trial simulations were conducted using the MBMA to predict seasonal incidence rates (IR) of RSV medically attended lower-respiratory tract infection (MALRI) and efficacies for a range of MK-1654 doses in both temperate and tropical regions.

Results

Epidemiological data was well captured by the FOI function. Clinical trial simulations indicated that seasonal IRs of RSV were sensitive to differences in the FOI represented by temperate and tropical regions; however, there was no substantial impact on efficacies across MK-1654 dose levels. Consistent with predictions for a temperate climate, MK-1654, when administered at the start of the RSV season in a region with a tropical climate, was also predicted to maintain high efficacy ( > 75%) for the prevention of RSV MALRI for 150 days.

Conclusion

Simulations indicated that while FOI is a substantial driver of overall RSV incidence rates, MK-1654 efficacy in a late-stage clinical trial is likely to be high, regardless of regional variations in RSV.

Disclosures

Nele Plock, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Jos Lommerse, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Brian M. Maas, PharmD, Merck & Co., Inc. (Employee, Shareholder) Jingxian Chen, PhD, Merck & Co., Inc. (Employee, Shareholder) Francesco Bellanti, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Li Qin, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Han Witjes, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Philippe Pierrillas, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Radha Railkar, PhD, Merck & Co., Inc. (Employee, Shareholder) Antonios O. Aliprantis, MD, PhD, Merck & Co., Inc. (Employee, Shareholder) Kalpit A. Vora, PhD, Merck & Co., Inc. (Employee, Shareholder) Wei Gao, PhD, Merck & Co., Inc. (Employee, Shareholder) Luzelena Caro, PhD, Merck & Co., Inc. (Employee, Shareholder) S. Y. Amy Cheung, PhD, Certara (Employee, Shareholder) Jeffrey R. Sachs, PhD, Merck & Co., Inc. (Employee, Shareholder)

998. Forward and Reverse Translational Approaches to Predict Efficacy of the Neutralizing Respiratory Syncytial Virus (RSV) Antibody MK-1654

Background

MK-1654 is a respiratory syncytial virus (RSV) F glycoprotein neutralizing monoclonal antibody (mAb) with an extended half-life in late development to prevent RSV infection in infants. Neutralizing mAbs, like MK-1654, have great potential for prophylaxis against viral infection. However, well-validated approaches for clinical dose and efficacy predictions are lacking.

Methods

Summary-level literature data from RSV prevention studies were used in a model-based meta-analysis (MBMA) to describe the relationship between RSV incidence rates and serum neutralizing antibody (SNA) titer. The model was validated using viral challenge experiments in cotton rats and phase 3 RSV-A efficacy results in infants for an anti-RSV F mAb, REGN-2222. A phase 2b human RSV challenge study (HCS) in adults was also conducted with MK-1654. Participants (N=70) received 100, 200, 300, or 900 mg of MK-1564 or placebo and were challenged intranasally with RSV 29 days later. RSV viral load and symptomatic infection were monitored. Data from the HCS were compared to model predictions. The MBMA was used to predict efficacy of MK-1654 in a virtual population of pre- and full- term infants.

Results

The relationship between SNA titer and RSV incidence rate defined using the viral load data from the cotton rat approximated the relationship identified for infants from the clinical MBMA. The MBMA was quantitatively consistent with the phase 3 efficacy results against RSV A for REGN-2222. In the HCS, RSV nasal viral load measured by RT-qPCR and quantitative culture as well as symptomatic infections were decreased in MK-1654 recipients compared to placebo. Incidence rates of RSV infection in the HCS were also consistent with MBMA predictions. The model-based clinical trial simulations for MK-1654 indicated a high probability of substantial efficacy against RSV-associated medically attended lower respiratory tract infection ( >75% for 5 months) for doses ≥75 mg.

Conclusion

Our MBMA successfully quantified the relationship between RSV SNA and clinically relevant endpoints, including lower respiratory tract infection in infants. MBMA-based efficacy predictions support continued development of the MK-1654 antibody for the prevention of RSV in infants.

Disclosures

Brian M. Maas, PharmD, Merck & Co., Inc. (Employee, Shareholder) Jos Lommerse, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Nele Plock, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Radha Railkar, PhD, Merck & Co., Inc. (Employee, Shareholder) S. Y. Amy Cheung, PhD, Certara (Employee, Shareholder) Luzelena Caro, PhD, Merck & Co., Inc. (Employee, Shareholder) Jingxian Chen, PhD, Merck & Co., Inc. (Employee, Shareholder) Wen Liu, MPH, Merck & Co., Inc. (Employee, Shareholder) Ying Zhang, PhD, Merck & Co., Inc. (Employee, Shareholder) Qinlei Huang, MS, Merck & Co., Inc. (Employee, Shareholder) Wei Gao, PhD, Merck & Co., Inc. (Employee, Shareholder) Li Qin, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Jie Meng, MSc, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Han Witjes, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Emilie Schindler, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Benjamin Guiastrennec, PharmD, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Francesco Bellanti, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Daniel Spellman, PhD, Merck & Co., Inc. (Employee, Shareholder) Brad Roadcap, MS, Merck & Co., Inc. (Employee, Shareholder) Amy Espeseth, PhD, Merck & Co., Inc. (Employee, Shareholder) S. Aubrey Stoch, MD, Merck & Co., Inc. (Employee, Shareholder) Eseng Lai, MD, PhD, Merck & Co., Inc. (Employee, Shareholder) Kalpit A. Vora, PhD, Merck & Co., Inc. (Employee, Shareholder) Antonios O. Aliprantis, MD, PhD, Merck & Co., Inc. (Employee, Shareholder) Jeffrey R. Sachs, PhD, Merck & Co., Inc. (Employee, Shareholder)

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.

Concentration-QT modeling 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.

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.

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.

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.

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.

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, pGSK3β, 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 pGSK3β (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.

Pediatric Extrapolation in Type 2 Diabetes: Future Implications of a Workshop

Extrapolation from adults to youth with type 2 diabetes (T2D) is challenged by differences in disease progression and manifestation. This manuscript presents the results of a mock-team workshop focused on examining the typical team-based decision process used to propose a pediatric development plan for T2D addressing the viability of extrapolation. The workshop was held at the American Society for Clinical Pharmacology and Therapeutics (ASCPT) in Orlando, Florida on March 21, 2018.

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.

BEECH: a dose-finding run-in followed by a randomised phase II study assessing the efficacy of AKT inhibitor capivasertib (AZD5363) combined with paclitaxel in patients with estrogen receptor-positive advanced or metastatic breast cancer, and in a PIK3CA mutant sub-population

BACKGROUND

BEECH investigated the efficacy of capivasertib (AZD5363), an oral inhibitor of AKT isoforms 1-3, in combination with the first-line weekly paclitaxel for advanced or metastatic estrogen receptor-positive (ER+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer, and in a phosphoinositide 3-kinase, catalytic, alpha polypeptide mutation sub-population (PIK3CA+).

PATIENTS AND METHODS

BEECH consisted of an open-label, phase Ib safety run-in (part A) in 38 patients with advanced breast cancer, and a randomised, placebo-controlled, double-blind, phase II expansion (part B) in 110 women with ER+/HER2- metastatic breast cancer. In part A, patients received paclitaxel 90 mg/m2 (days 1, 8 and 15 of a 28-day cycle) with capivasertib taken twice daily (b.i.d.) at two intermittent ascending dosing schedules. In part B, patients were randomly assigned, stratified by PIK3CA mutation status, to receive paclitaxel with either capivasertib or placebo. The primary end point for part A was safety to recommend a dose and schedule for part B; primary end points for part B were progression-free survival (PFS) in the overall and PIK3CA+ sub-population.

RESULTS

Capivasertib was well tolerated, with a 400 mg b.i.d. 4 days on/3 days off treatment schedule selected in part A. In part B, median PFS in the overall population was 10.9 months with capivasertib versus 8.4 months with placebo [hazard ratio (HR) 0.80; P = 0.308]. In the PIK3CA+ sub-population, median PFS was 10.9 months with capivasertib versus 10.8 months with placebo (HR 1.11; P = 0.760). Based on the Common Terminology Criteria for Adverse Event v4.0, the most common grade ≥3 adverse events in the capivasertib group were diarrhoea, hyperglycaemia, neutropoenia and maculopapular rash. Dose intensity of paclitaxel was similar in both groups.

CONCLUSIONS

Capivasertib had no apparent impact on the tolerability and dose intensity of paclitaxel. Adding capivasertib to weekly paclitaxel did not prolong PFS in the overall population or PIK3CA+ sub-population of ER+/HER2- advanced/metastatic breast cancer patients.ClinicalTrials.gov: NCT01625286.

Best Practices to Maximize the Use and Reuse of Quantitative and Systems Pharmacology Models: Recommendations From the United Kingdom Quantitative and Systems Pharmacology Network

The lack of standardization in the way that quantitative and systems pharmacology (QSP) models are developed, tested, and documented hinders their reproducibility, reusability, and expansion or reduction to alternative contexts. This in turn undermines the potential impact of QSP in academic, industrial, and regulatory frameworks. This article presents a minimum set of recommendations from the UK Quantitative and Systems Pharmacology Network (UK QSP Network) to guide QSP practitioners seeking to maximize their impact, and stakeholders considering the use of QSP models in their environment.

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.

A Phase I Open-Label Study to Identify a Dosing Regimen of the Pan-AKT Inhibitor AZD5363 for Evaluation in Solid Tumors and in PIK3CA-Mutated Breast and Gynecologic Cancers

Purpose: This phase I, open-label study (Study 1, D3610C00001; NCT01226316) was the first-in-human evaluation of oral AZD5363, a selective pan-AKT inhibitor, in patients with advanced solid malignancies. The objectives were to investigate the safety, tolerability, and pharmacokinetics of AZD5363, define a recommended dosing schedule, and evaluate preliminary clinical activity.Experimental Design: Patients were aged ≥18 years with World Health Organization (WHO) performance status of 0 to 1. Dose escalation was conducted within separate continuous and intermittent [4 days/week (4/7) or 2 days/week (2/7)] schedules with safety, pharmacokinetic, and pharmacodynamic analyses. Expansion cohorts of approximately 20 patients each explored AZD5363 activity in PIK3CA-mutant breast and gynecologic cancers.Results: MTDs were 320, 480, and 640 mg for continuous (n = 47), 4/7 (n = 21), and 2/7 (n = 22) schedules, respectively. Dose-limiting toxicities were rash and diarrhea for continuous, hyperglycemia for 2/7, and none for 4/7. Common adverse events were diarrhea (78%) and nausea (49%) and, for Common Terminology Criteria for Adverse Events grade ≥3 events, hyperglycemia (20%). The recommended phase II dose (480 mg bid, 4/7 intermittent) was assessed in PIK3CA-mutant breast and gynecologic expansion cohorts: 46% and 56% of patients, respectively, showed a reduction in tumor size, with RECIST responses of 4% and 8%. These responses were less than the prespecified 20% response rate; therefore, the criteria to stop further recruitment to the PIK3CA-mutant cohort were met.Conclusions: At the recommended phase II dose, AZD5363 was well tolerated and achieved plasma levels and robust target modulation in tumors. Proof-of-concept responses were observed in patients with PIK3CA-mutant cancers treated with AZD5363. Clin Cancer Res; 24(9); 2050-9. ©2017 AACRSee related commentary by Costa and Bosch, p. 2029.

Predictive Performance of Physiologically Based Pharmacokinetic (PBPK) Modeling of Drugs Extensively Metabolized by Major Cytochrome P450s in Children

The accuracy of physiologically based pharmacokinetic (PBPK) model prediction in children, especially those younger than 2 years old, has not been systematically evaluated. The aim of this study was to characterize the pediatric predictive performance of the PBPK approach for 10 drugs extensively metabolized by CYP1A2 (theophylline), CYP2C8 (desloratidine, montelukast), CYP2C9 (diclofenac), CYP2C19 (esomeprazole, lansoprazole), CYP2D6 (tramadol), and CYP3A4 (itraconazole, ondansetron, sufentanil). Model performance in children was evaluated by comparing simulated plasma concentration-time profiles with observed clinical results for each drug and age group. PBPK models reasonably predicted the pharmacokinetics of desloratadine, diclofenac, itraconazole, lansoprazole, montelukast, ondansetron, sufentanil, theophylline, and tramadol across all age groups. Collectively, 58 out of 67 predictions were within 2-fold and 43 out of 67 predictions within 1.5-fold of observed values. Developed PBPK models can reasonably predict exposure in children age 1 month and older for an array of predominantly CYP metabolized drugs.

Whole body physiologically based modelling of β-blockers in the rat: events in tissues and plasma following an i.v. bolus dose

BACKGROUND AND PURPOSE

Whole body physiologically based pharmacokinetic (PBPK) models have been increasingly applied in drug development to describe kinetic events of therapeutic agents in animals and humans. The advantage of such modelling is the ability to incorporate vast amounts of physiological information, such as organ blood flow and volume, to ensure that the model is as close to reality as possible.

EXPERIMENTAL APPROACH

Previous PBPK model development of enantiomers of a series of seven racemic β-blockers, namely, acebutolol, betaxolol, bisoprolol, metoprolol, oxprenolol, pindolol and propranolol, together with S-timolol in rat was based on tissue and blood concentration data at steady state. Compounds were administered in several cassettes with the composition mix and blood and tissue sampling times determined using a D-optimal design.

KEY RESULTS

Closed-loop PBPK models were developed initially based on the application of open loop forcing function models to individual tissues and compounds. For the majority of compounds and tissues, distribution kinetics was adequately characterized by perfusion rate-limited models. For some compounds in the testes and gut, a permeability rate-limited distribution model was required to best fit the data. Parameter estimates of the tissue-to-blood partition coefficient through fitting of individual enantiomers and of racemic pair were generally in agreement and also concur with those from previous steady-state experiments.

CONCLUSIONS AND IMPLICATIONS

PBPK modelling is a very powerful tool to aid drug discovery and development of therapeutic agents in animals and humans. However, careful consideration of the assumptions made during the modelling exercise is essential.

Dose-exposure-response relationship between AZD6738 and peripheral monocytes

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Background: AZD6738 is a potent, selective inhibitor of the ataxia telangiectasia and Rad3-related (ATR) serine/threonine-specific protein kinase with good selectivity against other phosphatidylinositol 3-kinase-related kinase (PIKK) family members. To support dose/schedule guidance through mathematical modelling of clinical pharmacokinetic/pharmacodynamic relationships, we sought to determine quantitative pharmacodynamic effects of AZD6738 in peripheral blood with respect to drug exposure. Methods: We assessed peripheral blood cell subpopulations in clinical studies of AZD6738 used either as a monotherapy, a combination with the PARP-1 inhibitor olaparib and in combination with the PD-L1 inhibitor durvalumab through both blood analyser and flow cytometry characterization. Peripheral pharmacokinetics of AZD6738 were also assessed. Results: Levels of peripheral monocytes were suppressed in a dose-dependent manner for the duration of AZD6738 dosing resulting in a decrease of up to 80% within the first 7 consecutive days of AZD6738 treatment; these results were consistent across studies. Upon cessation of an AZD6738 dosing interval monocyte levels return to baseline values within 14 days; in combination with durvalumab there is a trend for the rebound in monocyte counts to surpass baseline levels by up to 20%. Monocyte suppression is specific to AZD6738 and not found with either single agent olaparib or single agent durvalumab, possibly attributable to defective base excision repair in monocytes and lack of PARP-1. Monocytes were found to be the most sensitive cell type to AZD6738 treatment compared with neutrophils, platelets, haemoglobin or lymphocytes. Conclusions: This drug effect of AZD6738 is consistent with natural clearance of peripheral monocytes under suppression of monocyte precursor proliferation in the bone marrow, suggestive of a change in the equilibrium between production and elimination of peripheral monocytes. Used as a surrogate marker of target inhibition, a mathematical model of the AZD6738/monocyte dose-exposure-response relationship is described and has been used to inform AZD6738 Phase 2 dose and schedule.

Thoughtflow: Standards and Tools for Provenance Capture and Workflow Definition to Support Model-Informed Drug Discovery and Development

Pharmacometric analyses are complex and multifactorial. It is essential to check, track, and document the vast amounts of data and metadata that are generated during these analyses (and the relationships between them) in order to comply with regulations, support quality control, auditing, and reporting. It is, however, challenging, tedious, error-prone, and time-consuming, and diverts pharmacometricians from the more useful business of doing science. Automating this process would save time, reduce transcriptional errors, support the retention and transfer of knowledge, encourage good practice, and help ensure that pharmacometric analyses appropriately impact decisions. The ability to document, communicate, and reconstruct a complete pharmacometric analysis using an open standard would have considerable benefits. In this article, the Innovative Medicines Initiative (IMI) Drug Disease Model Resources (DDMoRe) consortium proposes a set of standards to facilitate the capture, storage, and reporting of knowledge (including assumptions and decisions) in the context of model-informed drug discovery and development (MID3), as well as to support reproducibility: "Thoughtflow." A prototype software implementation is provided.

Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation

This document was developed to enable greater consistency in the practice, application, and documentation of Model-Informed Drug Discovery and Development (MID3) across the pharmaceutical industry. A collection of "good practice" recommendations are assembled here in order to minimize the heterogeneity in both the quality and content of MID3 implementation and documentation. The three major objectives of this white paper are to: i) inform company decision makers how the strategic integration of MID3 can benefit R&D efficiency; ii) provide MID3 analysts with sufficient material to enhance the planning, rigor, and consistency of the application of MID3; and iii) provide regulatory authorities with substrate to develop MID3 related and/or MID3 enabled guidelines.

Structural identifiability and indistinguishability of compartmental models

The deterministic identifiability of models is only normally considered if a problem becomes apparent in the parameter identification stage of data analysis. If no problem is perceived then the analysis will continue. However, although the problem does not become apparent, the implications of ambiguities in what is inferred from the data should be considered. This paper reviews some fundamentals with respect to model indistinguishability and parameter identifiability.