Leveraging Modeling & Simulation for Pediatric Drug Development

Developing drugs for pediatrics has been historically challenging due to the vulnerable nature of this special population. When it comes to developing pediatric drugs, it is vitally important to understand the nuances inherent in the field. Gaining informed consent from families, encountering possible dissent from the children involved, and achieving acceptability from study personnel are all hurdles that drug developers may face that can contribute to the success or failure of a clinical study in children. In addition, ensuring the appropriate study designs, understanding the many ways in which pharmacokinetics (PK) and pharmacodynamics (PD) can differ in young people, establishing early engagement with regulatory authorities, and leveraging the appropriate modeling and simulation approaches are also all key factors critical for success.

Regulatory Guidance for Pediatric Study Designs

Over the years, studying drugs in pediatrics has become increasingly more incentivized and regulated with the addition of various legislative requirements by regulatory authorities such as the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). However, clinical studies in pediatrics remain complex and most drug development programs only have one chance to be successful in finding a safe and effective dose level. Contributing to the complexity is the fact that the dose range must cover the entire span of the pediatric age groups – from neonates (newborn infants) to adolescents (12 to <16 years old) and every age in between. In the draft guideline covering general clinical pharmacology considerations for pediatric studies for drugs and biological products, the FDA provides a pediatric study planning and extrapolation algorithm. This pediatric study decision tree helps determine when extrapolation is or is not appropriate for efficacy. At the minimum, a PK study and a safety trial will be required (which can sometimes be combined into one single trial) when efficacy can be extrapolated. If no extrapolation is possible, a dose-ranging study followed by an additional safety and efficacy trial in pediatrics will be expected.

Modeling Approaches Pivotal to Pediatric Development

Advances in PK/PD modeling and simulation tools have proven to be pivotal in pediatric drug development by supporting optimal trial designs. Model-informed drug development (MIDD) approaches help drug developers understand growth-related factors that can influence a drug’s pharmacology, thus optimizing the number of patients and duration of a study, minimizing the sampling burden for PK, biomarkers, and/or endpoints, and much more. Some important types of modeling and simulation approaches for pediatric drug development include:

• Population PK modeling
• Extrapolation and allometric scaling
• Clinical trial simulations

Population PK

An important modeling and simulation approach, known as population PK (popPK) modeling, is widely used to characterize the PK properties of drug and to explain the variability between subjects by evaluating the effects of “covariates” on the PK. Covariates can be intrinsic and extrinsic factors that influence a drug’s PK parameters, such as disease status, demographics, or concomitant medications. PopPK modeling in pediatric studies is crucial in two instances:

• to predict the adequate dosing regimen to be tested in a pediatric study (through extrapolation) based on prior safety and efficacy data from adults and/or pre-clinical models
• to analyze the PK data from the pediatric study itself

Once data from a pediatric study are available, refinement of the popPK model will allow for estimation of exposure metrics for each subject. Sparse PK sampling is often the norm in pediatric studies when the traditional non-compartmental analysis (NCA) approach, which requires rich sampling, is not possible. Furthermore, the updated popPK model may allow additional extrapolation to an even younger population than the pediatric age group investigated during the trial.

Extrapolation and Allometric Scaling

In pediatric modeling, extrapolation of PK data aims to scale a PK model from adults to children. Extrapolation requires a previously established model describing the adult data, in which appropriate covariate effects are included on PK parameters, such as clearance and volume of distribution, to account for developmental changes in pediatrics. Allometric scaling based on weight is widely used as an empirical approach for extrapolation; however, this method only accounts for size-related changes and may not be sufficient for capturing all growth and maturation-related changes in PK parameters, particularly for neonates and infants younger than age two. Physiological maturation of organs, both in size and function, together with specific enzymes and transporters involved in a drug’s PK are all important components to consider during pediatric extrapolation. Therefore, age is also a crucial covariate to account for in pediatric popPK modeling and extrapolation. In addition, for the youngest population of neonates and pre-term newborns, “age since birth” might not be the best predictor for organ maturation. Instead, post-menstrual age should be taken into consideration, offering more granularity in developmental changes of organs when the study includes young patients with a history of prematurity.

Clinical Trial Simulations

A clinical trial simulation platform creates virtual trials. Each trial uses random, virtual patients based on credible ages and weight ranges of interest (using growth charts from databases such as WHO or CDC). A popPK model can then be used to simulate PK profiles for each virtual patient for the dosing regimen being tested in each virtual trial. Some PK/PD relationships can also be part of the platform. Clinical trial simulations can establish the probability of study success based on the criteria that would be set for a real clinical trial. This method allows drug developers to virtually test different study designs and determine which one has the highest probability of success. Clinical trial simulations can also test various hypotheses and design scenarios to match adult exposures or to design dose-ranging studies across different age groups. This allows the trial design to be optimized to select the appropriate dose, number of subjects, or sampling schedules in order to reach the desired outcome. Furthermore, clinical trial simulations may help avoid some common mistakes in pediatric trials such as not enrolling enough subjects (or enrolling too many), testing uninformative dosing regimens for the exposure-response relationship, or testing outside of the therapeutic window. It should be noted that the pharmacodynamics (PD) (such as biomarkers or clinical endpoints) are not always well defined in pediatric populations. Clinical endpoints can also be different between adults and children. However, with some additional research, assumptions on any potential clinical endpoint variability may be introduced in the clinical trial simulation platform so that optimization can be achieved.

Benefits of Modeling & Simulation for Pediatrics

Modeling and simulation techniques, especially the popPK and extrapolation approaches described above, are regularly used to assist in pediatric trial design and optimization. However, popPK can be a much more powerful tool for overall pediatric drug development, alone or in combination with other model-based approaches. Some benefits that modeling and simulation provide for pediatric studies include:

• Reducing the duration and number of pediatric trials overall
• Reducing the sampling burden and number of pediatric patients overall
• Avoiding testing uninformative and unnecessary dosing regimens in pediatric patients

Clinical trial simulations in combination with popPK modeling and extrapolation can help estimate PK parameters for pediatric patients with sufficient precision such as prescribed by the draft FDA guideline. The information gained from modeling and simulation can lead to conducting fewer and shorter pediatric studies by optimizing sampling and maximizing the information collected on a drug’s PK and PD. Additionally, modeling tools can help define the exact time points or time windows at which blood samples should be taken. By minimizing the number of blood samples collected and maximizing the information collected to create a robust popPK model the blood draw burden can be significantly reduced for pediatric patients.

Developing Drugs for Pediatric-Specific Diseases

Some diseases are pediatric-specific. Certain forms of pediatric cancers have no adult equivalent, and some diseases have few consequences in adults, but can be very serious in infants such as respiratory syncytial virus (RSV) infection. Sometimes adult patients do not exist because of high mortality at a younger age as observed in certain neuromuscular dystrophies such as Duchenne muscular dystrophy (DMD). In these cases, the most efficient way to determine pediatric dose will be in studies directly involving young pediatric patients (where the youngest age is the ultimate target population) instead of traditional studies with age group de-escalation. Model-based approaches can then provide the rationale for an initial dose and dose escalation in pediatric patients based on pre-clinical findings, adult data from healthy volunteers (if available), and any relevant literature data. The objective will be to minimize the number of suboptimal doses and collect as much valuable information as possible on both PK and PD. For example, using an adaptive design based on real-time analysis and modeling of the study data in a first-time-in-infant pediatric trial allows the dose-escalation to be refined where needed, saving time and unnecessary burden for the pediatric infant patients.

Conclusions

Pediatric drug development can greatly benefit from multiple types of modeling and simulation methods. The popPK approach is ideal to study PK in children with sparse data obtained from heterogeneous age groups. PK/PD extrapolation and allometric scaling provide a rationale for dose choices, whether the efficacy is known in adults or not. Finally, clinical trial simulation platforms will optimize study designs to minimize the length and cost of studies and to maximize clinical trial success. Nuventra’s team of experienced pharmacometricians and modeling experts understand how important it is to utilize modeling and simulation approaches in the vulnerable patient population of pediatrics. We have experience using modeling to optimize pediatric studies and sometimes even avoid additional and unnecessary studies altogether. Contact us to learn more about our modeling and simulation services for pediatric drug development.