Dose rationale for the use of meropenem/vaborbactam combination in paediatric patients with Gram-negative bacterial infections.

AIMS: Meropenem/vaborbactam combination is approved in adults by FDA and EMA for complicated urinary tract infections and by EMA also for other Gram-negative infections. We aimed to characterise the pharmacokinetics of both moieties in an ongoing study in children and use a model-based approach to inform adequate dosing regimens in paediatric patients. METHODS: Over 4196 blood samples of meropenem and vaborbactam (n = 414 subjects) in adults, together with 114 blood samples (n = 39) in paediatric patients aged 3 months to 18 years were available for this analysis. Data were analysed using a population with prior information from a pharmacokinetic model in adults to inform parameter estimation in children. Simulations were performed to assess the suitability of different dosing regimens to achieve adequate probability of target attainment (PTA). RESULTS: Meropenem/vaborbactam PK was described with two-compartment models with first-order elimination. Body weight and CLcr were significant covariates on the disposition of both drugs. A maturation function was evaluated to explore changes in clearance in neonates. PTA ≥90% was derived for children aged ≥3 months after 3.5-h IV infusion of 40 mg/kg Q8h of both meropenem and vaborbactam and 2 g/2 g for those ≥50 kg. Extrapolation of disposition parameters suggest that adequate PTA is achieved after a 3.5-h IV infusion of 20 mg/kg for neonates and infants (3 months). CONCLUSIONS: An integrated analysis of adult and paediatric data allowed accurate description of sparsely sampled meropenem/vaborbactam PK in paediatric patients and provided recommendations for the dosing in neonates and infants (3 months).

Characterisation of individual ferritin response in patients receiving chelation therapy.

AIMS: To develop a drug-disease model describing iron overload and its effect on ferritin response in patients affected by transfusion-dependent haemoglobinopathies and investigate the contribution of interindividual differences in demographic and clinical factors on chelation therapy with deferiprone or deferasirox. METHODS: Individual and mean serum ferritin data were retrieved from 13 published studies in patients affected by haemoglobinopathies receiving deferiprone or deferasirox. A nonlinear mixed effects modelling approach was used to characterise iron homeostasis and serum ferritin production taking into account annual blood consumption, baseline demographic and clinical characteristics. The effect of chelation therapy was parameterised as an increase in the iron elimination rate. Internal and external validation procedures were used to assess model performance across different study populations. RESULTS: An indirect response model was identified, including baseline ferritin concentrations and annual blood consumption as covariates. The effect of chelation on iron elimination rate was characterised by a linear function, with different slopes for each drug (0.0109 [90% CI: 0.0079-0.0131] vs. 0.0013 [90% CI: 0.0008-0.0018] L/mg mo). In addition to drug-specific differences in the magnitude of the ferritin response, simulation scenarios indicate that ferritin elimination rates depend on ferritin concentrations at baseline. CONCLUSION: Modelling of serum ferritin following chronic blood transfusion enabled the evaluation of drug-induced changes in iron elimination rate and ferritin production. The use of a semi-mechanistic parameterisation allowed us to disentangle disease-specific factors from drug-specific properties. Despite comparable chelation mechanisms, deferiprone appears to have a significantly larger effect on the iron elimination rate than deferasirox.

Model-based prediction of effective target exposure for MEN1611 in combination with trastuzumab in HER2-positive advanced or metastatic breast cancer patients.

MEN1611 is a novel orally bioavailable PI3K inhibitor currently in clinical development for patients with HER2-positive (HER2+) PI3KCA mutated advanced/metastatic breast cancer (BC) in combination with trastuzumab (TZB). In this work, a translational model-based approach to determine the minimum target exposure of MEN1611 in combination with TZB was applied. First, pharmacokinetic (PK) models for MEN1611 and TZB in mice were developed. Then, in vivo tumor growth inhibition (TGI) data from seven combination studies in mice xenograft models representative of the human HER2+ BC non-responsive to TZB (alterations of the PI3K/AkT/mTOR pathway) were analyzed using a PK-pharmacodynamic (PD) TGI model for co-administration of MEN1611 and TZB. The established PK-PD relationship was used to quantify the minimum effective MEN1611 concentration, as a function of TZB concentration, needed for tumor eradication in xenograft mice. Finally, a range of minimum effective exposures for MEN1611 were extrapolated to patients with BC, considering the typical steady-state TZB plasma levels in patients with BC following three alternative regimens (i.v. 4 mg/kg loading dose +2 mg/kg q1w, i.v. 8 mg/kg loading dose +6 mg/kg q3w or s.c. 600 mg q3w). A threshold of about 2000 ng·h/ml for MEN1611 exposure associated with a high likelihood of effective antitumor activity in a large majority of patients was identified for the 3-weekly and the weekly i.v. schedule for TZB. A slightly lower exposure (i.e., 25% lower) was found for the 3-weekly s.c. schedule. This important outcome confirmed the adequacy of the therapeutic dose administered in the ongoing phase 1b B-PRECISE-01 study in patients with HER2+ PI3KCA mutated advanced/metastatic BC.

Use of prior knowledge and extrapolation in paediatric drug development: A case study with deferasirox.

The characterisation of pharmacokinetics, pharmacodynamics and dose-exposure-response relationships requires data arising from well-designed study protocols and a relatively large sample from the target patient population. Such a prerequisite is unrealistic for paediatric rare diseases, where the patient population is often vulnerable and very small. In such cases, different sources of data and knowledge need to be considered to ensure trial designs are truly informative and oncoming data can be analysed efficiently. Here, we use clinical trial simulations to assess the contribution of historical data for (1) the analysis of sparse samples from a limited number of children and (2) the optimisation of study design when an increase in the number of subjects is not feasible. The evaluation of the pharmacokinetics of deferasirox in paediatric patients affected by haemoglobinopathies was used as case study. Our investigation shows that the incorporation of prior knowledge increases parameter precision and probability of successful convergence from only 12% with no priors to 56% and 75% for weakly and highly informative priors, respectively. In addition, results suggest that even when only one sample is collected per subject, as implemented in the original trial and in many other examples in clinical research, there is a 60% probability of biased parameter estimates (>25%). In conjunction with adult prior information and optimisation techniques, the probability of bias could be limited to <20% by increasing the number of samples/subject from 1 to 3. The methodology described here can be easily applied to other studies in small populations.

Prediction of the Effect of Renal Impairment on the Pharmacokinetics of New Drugs.

INTRODUCTION: Renal impairment may have a significant impact on the pharmacokinetics of drugs. Ad hoc studies in subjects with renal impairment are required by the regulatory authorities to propose dose adjustments in these subjects, to find a dosing regimen able to provide a systemic exposure similar to those in subjects with a normal renal function given the relevant clinical dose. METHODS: To evaluate the main descriptors and establish a predictive model of the effect of renal impairment on the exposure of new drugs, we considered 73 marketed drugs, for which studies in subjects with different degrees of renal impairment were available in the literature. Multivariate analysis was performed using the main pharmacokinetic parameters. Other approaches, including data mining and machine learning techniques, were tested to propose models based on a categorical definition of the exposure changes. RESULTS: Stepwise multivariate regression analyses revealed, as expected, that the fraction of dose excreted unchanged in urine and plasma protein binding were the factors primarily related to the change in exposure between subjects with normal and impaired renal function. Data mining techniques provided similar results. DISCUSSION: The pharmacokinetic predictions were however not always satisfactory, especially for drugs which, despite the negligible renal excretion, are characterized by significant increases in the systemic exposure in subjects with renal impairment. This phenomenon, interpreted considering the accumulation of endogenous metabolism inhibitors in subjects with moderate and severe renal disease (uremic toxins), cannot be fully captured and described, likely owing to an incomplete understanding of the pathophysiological phenomena and to some limitations of the available database of clinical studies.

Complex Bayesian Modeling Workflows Encoding and Execution Made Easy With a Novel WinBUGS Plugin of the Drug Disease Model Resources Interoperability Framework.

The Drug Disease Model Resources (DDMoRe) Interoperability Framework (IOF) enables pharmacometric model encoding and execution via Model Description Language (MDL) and R language, through the ddmore package. Through its components and converter plugins, the IOF can execute pharmacometric tasks using different target tools, starting from a single MDL-encoded model. In this article, we present the WinBUGS plugin and show how its integration in the IOF enables an easy implementation of complex Bayesian workflows. We selected a published diabetes-linked study as a real-world example, in which two inter-related models are used to estimate insulin secretion rate in response to a glucose stimulus from intravenous glucose tolerance test (IVGTT) data. This model was implemented following different approaches to propagate uncertainty, via diverse IOF target tools (NONMEM, WinBUGS, PsN, and Xpose). The developed software supports a plethora of pharmacokinetic/pharmacodynamic (PK/PD) modeling features. It provides solutions to reproducibility and interoperability issues in Bayesian modeling, and facilitates the difficult encoding of complex PK/PD models in WinBUGS.

Mathematical modeling of efficacy and safety for anticancer drugs clinical development.

INTRODUCTION: Drug attrition in oncology clinical development is higher than in other therapeutic areas. In this context, pharmacometric modeling represents a useful tool to explore drug efficacy in earlier phases of clinical development, anticipating overall survival using quantitative model-based metrics. Furthermore, modeling approaches can be used to characterize earlier the safety and tolerability profile of drug candidates, and, thus, the risk-benefit ratio and the therapeutic index, supporting the design of optimal treatment regimens and accelerating the whole process of clinical drug development. Areas covered: Herein, the most relevant mathematical models used in clinical anticancer drug development during the last decade are described. Less recent models were considered in the review if they represent a standard for the analysis of certain types of efficacy or safety measures. Expert opinion: Several mathematical models have been proposed to predict overall survival from earlier endpoints and validate their surrogacy in demonstrating drug efficacy in place of overall survival. An increasing number of mathematical models have also been developed to describe the safety findings. Modeling has been extensively used in anticancer drug development to individualize dosing strategies based on patient characteristics, and design optimal dosing regimens balancing efficacy and safety.

Current mathematical models for cancer drug discovery.

INTRODUCTION: Pharmacometric models represent the most comprehensive approaches for extracting, summarizing and integrating information obtained in the often sparse, limited, and less-than-optimally designed experiments performed in the early phases of oncology drug discovery. Whilst empirical methodologies may be enough for screening and ranking candidate drugs, modeling approaches are needed for optimizing and making economically viable the learn-confirm cycles within an oncology research program and anticipating the dose regimens to be investigated in the subsequent clinical development. Areas covered: Papers appearing in the literature of approximately the last decade reporting modeling approaches applicable to anticancer drug discovery have been listed and commented. Papers were selected based on the interest in the proposed methodology or in its application. Expert opinion: The number of modeling approaches used in the discovery of anticancer drugs is consistently increasing and new models are developed based on the current directions of research of new candidate drugs. These approaches have contributed to a better understanding of new oncological targets and have allowed for the exploitation of the relatively sparse information generated by preclinical experiments. In addition, they are used in translational approaches for guiding and supporting the choice of dosing regimens in early clinical development.