Home / Resources / Blog / Quantitative Considerations for Pediatric Oncology Drug Discovery & Development

Quantitative Considerations for Pediatric Oncology Drug Discovery & Development

Due to the rapid and continuous physiological changes during growth and development, pediatric patients cannot be treated as “small adults.” These physiological and molecular differences include body composition (height and weight), organ function and size, receptor response (pathway, safety), and maturation of absorption, distribution, metabolism, and excretion (ADME) changes.

In addition to ontogeny considerations, pediatric oncology research faces other unique challenges. The low prevalence of pediatric cancer and restriction of total blood volume samplings for various age group limits the data available.  Invasive procedures should be minimized when possible in children, especially when they are unwell. Ultimately, unnecessary clinical trials should be avoided. If possible, we also want to understand how intrinsic/extrinsic factors such as tumor mutations impact treatment outcomes.

Model-informed drug discovery and development (MID3)

Model-informed drug discovery and development uses mathematical models and quantitative techniques to leverage existing and emerging nonclinical and clinical data to support better drug development decisions. This MID3 approach to pediatric research can help address the previously mentioned challenges.  MID3 helps to increase understanding of the relationship of various biomarkers with efficacy endpoints. In addition, not all data from all age groups are informative for younger groups because drug responses depend on the stage of disease progression and mechanism of action of the drug. The opportunities and challenges that arise in using MID3 approaches for pediatric research were presented at an American Society for Clinical Pharmacology and Therapeutics pre-conference meeting in 2018 (see Figure 1).

Increased understanding of tumor biology, especially molecular drivers, could lead to immunotherapy being used as precision medicine. It would enable us to understand disease progression and differences in diseases in subpopulations such as pediatrics. The availability of data on emerging biomarkers also encourages the use of a model-informed approach to relate dose-exposure, dose-response, and ultimately dose-exposure, response relationships. Research literature is often used to inform model-based meta-analysis. Finally, using model-informed approaches can support strategies for drug combination regimens, either combining novel treatments together or with the standard of care.

The MID3 approach is strongly endorsed by regulators and the pharmaceutical industry to support pediatric drug development. The International Council for Harmonisation (ICH) E11(R1) addendums also give guidance for applying modeling and simulation to pediatric drug development. Early pediatric strategy should include multidisciplinary experts in the use of modeling, available data, and the assumed clinical setting.

Empirical vs mechanistic approaches for dose selection

Key dose and regimen finding questions in pediatric oncology trials are still challenging, despite the availability of various quantitative approaches. For example, the recommended Phase 2 dose is often higher than the starting dose, which could possibly be due to the uncertainty of the drug exposures and safety of the starting dose. In this situation, dose escalation will take a lot longer. The method most commonly used for setting the starting dose is based on an empirical dose scaling approach, rather than utilizing all the information by applying a population pharmacokinetic (PK) or physiologically-based pharmacokinetic (PBPK) modeling approach if exposure matching is deemed to be feasible.

When scaling adult doses to a pediatric population (e.g. < 2 years and especially with neonates), maturation and physiology need to be considered with a PBPK approach, which allows these differences to be incorporated into the predictions. In a typical PBPK workflow, the starting point is normally a model validated based on adult PK data, the system parameters, and physiochemical drug information. Then the physiological parameters are ordered to reflect the pediatric age range and ontogeny being considered, which allows drug exposures to be predicted and a potentially effective starting dose to be selected. For older age groups, a weight-based scaling approach using population PK may be appropriate if weight is found to be a significant covariate.

Using MID3 in pediatric oncology development

There are several examples in the literature where PBPK approaches have been used to assist pediatric drug development. This approach ranges from selecting a starting dose in a first-in-pediatric trial to evaluate the factors that might influence drug exposure in children to optimizing trial design; for example, selecting the PK sampling schedule and windows.

As mentioned, we have a limited number of pediatric oncology patients due to the low prevalence and heterogeneity of disease. We can take advantage of precision oncology trial designs such as “basket and umbrella trials,” which investigate several drugs in multiple tumor types to detect signals and confirm drug mechanisms and mutation prevalence. These approaches are often applied to adult populations to test new anti-cancer agents in a more effective way. For example, the iMATRIX-atexolizumab study matched tumor biology with the mechanism of action of drugs to enroll patients into the trial. Population modeling using existing data demonstrated similar exposure-safety profiles in pediatrics and young adults, thus enabling a weight-based dosing approach. Innovative adaptive trial designs that incorporate a model-informed approach allow more efficient dose escalation in Phase 1 pediatric trials.

The IQ Clinical Pharmacology Leadership Group Pediatric Working Group published a white paper that explains the role of extrapolation in pediatric development. There are examples of different therapeutic areas, including oncology. The paper found that safety data in children is still required in many cases because of the concerns for potential toxicity of anti-cancer treatments. However, extrapolation of the drug exposures can still be especially useful to support starting dose selection and escalation. This paper also gave information on applying MID3 strategy to support a pediatric extrapolation plan. When developing a new targeted treatment for pediatric cancer, there are uncertainties in the relevance of the drug’s mechanism and the required drug exposures. Nonclinical models of cancer have supported drug discovery and developments for many years in the adult setting. The Innovative Therapies for Children with Cancer Pediatric Preclinical Proof-of-concept Platform (IMI ITCC P4) aims to develop and characterize animal models for pediatric cancer that will ultimately support pediatric cancer drug development by using the data for translational pharmacology (see Figure 2). It will also guide us on when in drug development to start the pediatric plan.

Figure 2: WP = work packages

Points to consider in pediatric oncology drug development

Cancer in children is rare but presents a high unmet need due to limited treatment options, the heterogeneity of cancer, and the aggressiveness of the disease in children. Pediatric studies cannot be waived for a drug program based upon the class of treatment if the mechanism is thought to be relevant in pediatric cancer. Thus, it’s important to evaluate the drug mechanism in pediatric cancer as early as possible and whether similar drug exposure as adult cancer patients is required for efficacy in pediatrics. Therefore, drug development must be efficient to deliver a new treatment to pediatric patients or to stop the development as early as possible if the treatment does not show promise for pediatrics. We must couple efficient trial designs with quantitative approaches such as MID3 to minimize the number of pediatric patients required and the time needed to answer important clinical questions to accelerate much needed cancer treatments to our youngest patients.

To learn more about best practices for fostering pediatric oncology drug development, please watch this webinar I gave on this topic.


References

Draft Guidance, Paediatric PK Study, FDA 1998.

Kearns GL, Abdel-Rahman SM, Alander SW, et al. Developmental pharmacology — drug disposition, action, and therapy in Infants and children. N Engl J Med. 2003;349:1157-1167.

Corriol-Rohou S, Cheung SYA. Industry Perspective on using MIDD for pediatric studies requiring integration of ontogeny. J Clin Pharm 2019;59:S112-S119.

Reaman G. American Society for Clinical Pharmacology & Therapeutics. 2018

IQ Consortium Clinical Pharmacology Leadership Group – Pediatric Working Group: Strategies to Address Opportunities in Oncology Pediatric Drug Development Webinar. 2018.

FDA Guidance. E11(R1) Addendum to E11: E11(R1) Addendum: Clinical Investigation of Medicinal Products in the Pediatric Population. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/e11r1-addendum-clinical-investigation-medicinal-products-pediatric-population accessed 30 Aug 2020.

Leong R, Zhao H, Reaman G, et al, Bridging adult experience to pediatric oncology drug development. J of Clin Pharm. 2017;57(S10):S129-S135.

Manolis E, Osman TE, Herold R, et al. Role of modeling and simulation in pediatric investigation plans. Ped Anes. 2011;21:214-221.

Masoud J, Marciniak S, Edwards D, et al. The Simcyp Population Based Simulator: Architecture, Implementation, and Quality Assurance. In Silico Pharmacol. 2013 Jun. doi: 10.1186/2193-9616-1-9

Rioux N, Waters NJ. Physiologically based pharmacokinetic modeling in pediatric oncology drug development. Drug Met and Dis. 2016;44:934-943.

Khan T, Karski E, Caron H, et al. Mechanism of action based master trial approach in pediatric oncology. 2017 https://www.focr.org/sites/default/files/iMatrix%20Concept%20Paper.pdf. Accessed September 2017.

Khan, T., Stewart, M., Blackman, S. et al. Accelerating Pediatric Cancer Drug Development: Challenges and Opportunities for Pediatric Master Protocols. Ther Innov Regul Sci 53, 270–278 (2019). https://doi.org/10.1177/2168479018774533

Forrest SJ, Geoerger B, Janeway K. Precision medicine in pediatric oncology. Curr Opin Pediatr. 2018;30:17-24.

S. Simko, ASCPT 2018. From session: Pediatric Drug development: Challenges and opportunity in extrapolation

Shemesh CS, Chanu P, Jamsen K, et al. Population pharmacokinetics, exposure-safety and immunogenicity of atezolizumab in pediatric and young adults patients with cancer. J Immunother Cancer. 2019;314. doi.org/10.1186/s40425-019-0791-x. Musuamba, F., Manolis, E., Holford, N., et al. Advanced Methods for Dose and Regimen Finding During Drug Development: Summary of the EMA/EFPIA Workshop on Dose Finding (London 4–5 December 2014). CPT Pharmacometrics Syst. Pharmacol., 2017; 6: 418-429. https://doi.org/10.1002/psp4.12196

About the author

By: S.Y. Amy Cheung

Back to top
Powered by Translations.com GlobalLink OneLink Software