Model-informed Drug Development Research Articles

Challenging the Norm: A Multidisciplinary Perspective on Intravenous to Subcutaneous Bridging Strategies for Biologics.

The transition from intravenous (i.v.) to subcutaneous (s.c.) administration of biologics is a critical strategy in drug development aimed at improving patient convenience, compliance, and therapeutic outcomes. Focusing on the increasing role of model-informed drug development (MIDD) in the acceleration of this transition, an in-depth overview of the essential clinical pharmacology, and regulatory considerations for successful i.v. to s.c. bridging for biologics after the i.v. formulation has been approved are presented. Considerations encompass multiple aspects beginning with adequate pharmacokinetic (PK) and pharmacodynamic (i.e., exposure-response) evaluations which play a vital role in establishing comparability between the i.v. and s.c. routes of administrations. Selected key recommendations and points to consider include: (i) PK characterization of the s.c. formulation, supported by the increasing preclinical understanding of the s.c. absorption, and robust PK study design and analyses in humans; (ii) a thorough characterization of the exposure-response profiles including important metrics of exposure for both efficacy and safety; (iii) comparability studies designed to meet regulatory considerations and support approval of the s.c. formulation, including noninferiority studies with PK and/or efficacy and safety as primary end points; and (iv) comprehensive safety package addressing assessments of immunogenicity and patients' safety profile with the new route of administration. Recommendations for successful bridging strategies are evolving and MIDD approaches have been used successfully to accelerate the transition to s.c. dosing, ultimately leading to improved patient experiences, adherence, and clinical outcomes.

Potassium homeostasis and therapeutic intervention with sodium zirconium cyclosilicate: A model-informed drug development case study.

Potassium (K+ ) is the main intracellular cation in the body. Elevated K+ levels (hyperkalemia) increase the risk of life-threatening arrhythmias and sudden cardiac death. However, the details of K+ homeostasis and the effects of orally administered K+ binders, such as sodium zirconium cyclosilicate (SZC), on K+ redistribution and excretion in patients remain incompletely understood. We built a fit-for-purpose systems pharmacology model to describe K+ homeostasis in hyperkalemic subjects and capture serum K+ (sK+ ) dynamics in response to acute and chronic administration of SZC. The resulting model describes K+ distribution in the gastrointestinal (GI) tract, blood, and extracellular and intracellular spaces of tissue, renal clearance of K+ , and K+ -SZC binding and excretion in the GI tract. The model, which was fit to time-course sK+ data for individual patients from two clinical trials, accounts for bolus delivery of K+ in meals and oral doses of SZC. The virtual population of patients derived from fitting the model to these trials was then modified to predict the SZC dose-response and inform clinical trial design in two new applications: emergency lowering of sK+ in severe hyperkalemia and prevention of hyperkalemia between dialysis sessions in patients with end-stage chronic kidney disease. In both cases, the model provided novel and useful insight that was borne out by the now completed clinical trials, providing a concrete case study of fit-for-purpose, model-informed drug development after initial approval of a drug.

Bridging responses to a human telomerase reverse transcriptase-based peptide cancer vaccine candidate in a mechanism-based model

Therapeutic cancer vaccines are novel immuno-therapeutics, aiming to improve clinical outcomes with other immunotherapies. However, obstacles to their successful clinical development remain, which model-informed drug development approaches may address. UV1 is a telomerase based therapeutic cancer vaccine candidate being investigated in phase I clinical trials for multiple indications. We developed a mechanism-based model structure, using a nonlinear mixed‐effects modeling techniques, based on longitudinal tumor sizes (sum of the longest diameters, SLD), UV1-specific immunological assessment (stimulation index, SI) and overall survival (OS) data obtained from a UV1 phase I trial including non-small cell lung cancer (NSCLC) patients and a phase I/IIa trial including malignant melanoma (MM) patients. The final structure comprised a mechanistic tumor growth dynamics (TGD) model, a model describing the probability of observing a UV1-specific immune response (SI ≥ 3) and a time-to-event model for OS. The mechanistic TGD model accounted for the interplay between the vaccine peptides, immune system and tumor. The model-predicted UV1-specific effector CD4+ T cells induced tumor shrinkage with half-lives of 103 and 154 days in NSCLC and MM patients, respectively. The probability of observing a UV1-specific immune response was mainly driven by the model-predicted UV1-specific effector and memory CD4+ T cells. A high baseline SLD and a high relative increase from nadir were identified as main predictors for a reduced OS in NSCLC and MM patients, respectively. Our model predictions highlighted that additional maintenance doses, i.e. UV1 administration for longer periods, may result in more sustained tumor size shrinkage.

Rare oncology therapeutics: review of clinical pharmacology package of drug approvals (2019-2023) by US FDA, best practices and recommendations.

There are many challenges with rare diseases drug development and rare oncology indications are not different. To understand the regulatory landscape as it relates to application of clinical pharmacology principles in rare oncology product development, we reviewed publicly available information of 39 approvals by US FDA between January 2019 and March 2023. The objective was to understand the expected clinical pharmacology studies and knowledge base in such approvals. Model informed drug development (MIDD) applications were also reviewed, as such approaches are expected to play a critical role in filling clinical pharmacology gaps in rare oncology, where number of clinical trials and size of these trials will perhaps continue to be small. The findings highlighted how clinical pharmacology contributed to the evidence of effectiveness, dose optimization and elucidation of intrinsic and extrinsic factors affecting drug's behavior. Clinical pharmacology studies were often integrated with modeling in many of the NDAs/BLAs. Of the post marketing requirements (PMR) received, 18% were for dose optimization, 49% for DDI, 8% for QTc, 49% for specific population, and 5% for food effect. Two post marketing commitments (PMC) were issued for immunogenicity of the 11 biologics submissions. 15% (6 of 39) of the submissions used maximum tolerated dose (MTD) to advance their molecule into Phase 2 studies. Of them 3 approvals received PMR for dose optimization. 3 + 3 was the most prevalent Phase 1 design with use in 74% of the New Drug Applications (NDA)/Biologic License Applications (BLA) reviewed. Rest used innovative approaches such as BLRM, BOIN or mTPi, with BLRM being the most common. Seamless clinical pharmacology and MIDD approaches are paramount for rare oncology drug development.

Prospective approaches to gene therapy computational modeling – spotlight on viral gene therapy

Clinical studies have found there still exists a lack of gene therapy dose-toxicity and dose-efficacy data that causes gene therapy dose selection to remain elusive. Model informed drug development (MIDD) has become a standard tool implemented throughout the discovery, development, and approval of pharmaceutical therapies, and has the potential to inform dose-toxicity and dose-efficacy relationships to support gene therapy dose selection. Despite this potential, MIDD approaches for gene therapy remain immature and require standardization to be useful for gene therapy clinical programs. With the goal to advance MIDD approaches for gene therapy, in this review we first provide an overview of gene therapy types and how they differ from a bioanalytical, formulation, route of administration, and regulatory standpoint. With this biological and regulatory background, we propose how MIDD can be advanced for AAV-based gene therapies by utilizing physiological based pharmacokinetic modeling and quantitative systems pharmacology to holistically inform AAV and target protein dynamics following dosing. We discuss how this proposed model, allowing for in-depth exploration of AAV pharmacology, could be the key the field needs to treat these unmet disease populations.

Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices.

Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered in a new era of possibilities across various scientific domains. One area where these advancements hold significant promise is model-informed drug discovery and development (MID3). To foster a wider adoption and acceptance of these advanced algorithms, the Innovation and Quality (IQ) Consortium initiated the AI/ML working group in 2021 with the aim of promoting their acceptance among the broader scientific community as well as by regulatory agencies. By drawing insights from workshops organized by the working group and attended by key stakeholders across the biopharma industry, academia, and regulatory agencies, this white paper provides a perspective from the IQ Consortium. The range of applications covered in this white paper encompass the following thematic topics: (i) AI/ML-enabled Analytics for Pharmacometrics and Quantitative Systems Pharmacology (QSP) Workflows; (ii) Explainable Artificial Intelligence and its Applications in Disease Progression Modeling; (iii) Natural Language Processing (NLP) in Quantitative Pharmacology Modeling; and (iv) AI/ML Utilization in Drug Discovery. Additionally, the paper offers a set of best practices to ensure an effective and responsible use of AI, including considering the context of use, explainability and generalizability of models, and having human-in-the-loop. We believe that embracing the transformative power of AI in quantitative modeling while adopting a set of good practices can unlock new opportunities for innovation, increase efficiency, and ultimately bring benefits to patients.

Whole-Body Disposition and Physiologically Based Pharmacokinetic Modeling of Adeno-Associated Viruses and the Transgene Product

To facilitate model-informed drug development (MIDD) of adeno-associated virus (AAV) therapy, here we have developed a physiologically based pharmacokinetic (PBPK) model for AAVs following preclinical investigation in mice. After 2E11 Vg/mouse dose of AAV8 and AAV9 encoding a monoclonal antibody (mAb) gene, whole-body disposition of both the vector and the transgene mAb was evaluated over 3 weeks. At steady-state, the following tissue-to-blood (T/B) concentration ratios were found for AAV8/9: ∼50 for liver; ∼10 for heart and muscle; ∼2 for brain, lung, kidney, adipose, and spleen; ≤1 for bone, skin, and pancreas. T/B values for mAb were compared with the antibody biodistribution coefficients, and five different clusters of organs were identified based on their transgene expression profile. All the biodistribution data were used to develop a novel AAV PBPK model that incorporates: (i) whole-body distribution of the vector; (ii) binding, internalization, and intracellular processing of the vector; (iii) transgene expression and secretion; and (iv) whole-body disposition of the secreted transgene product. The model was able to capture systemic and tissue PK of the vector and the transgene-produced mAb reasonably well. Pathway analysis of the PBPK model suggested that liver, muscle, and heart are the main contributors for the secreted transgene mAb. Unprecedented PK data and the novel PBPK model developed here provide the foundation for quantitative systems pharmacology (QSP) investigations of AAV-mediated gene therapies. The PBPK model can also serve as a quantitative tool for preclinical study design and preclinical-to-clinical translation of AAV-based gene therapies.

Applications of Modeling and Simulation Approaches in Support of Drug Product Development of Oral Dosage Forms and Locally Acting Drug Products: a Symposium Summary.

The number of modeling and simulation applications, including physiologically based pharmacokinetic (PBPK) models, physiologically based biopharmaceutics modeling (PBBM), and empirical models, has been constantly increasing along with the regulatory acceptance of these methodologies. While aiming at minimizing unnecessary human testing, these methodologies are used today to support the development and approval of novel drug products and generics. Modeling approaches are leveraged today for assessing drug-drug interaction, informing dose adjustments in renally or hepatically impaired patients, perform dose selection in pediatrics and pregnant women and diseased populations, and conduct biopharmaceutics-related assessments such as establish clinically relevant specifications for drug products and achieve quality assurance throughout the product life cycle. In the generics space, PBPK analyses are utilized toward virtual bioequivalence assessments within the scope of alternative bioequivalence approaches, product-specific guidance development, and food effect assessments among others. Case studies highlighting the evolving and expanding role of modeling and simulation approaches within the biopharmaceutics space were presented at the symposium titled "Model Informed Drug Development (MIDD): Role in Dose Selection, Vulnerable Populations, and Biowaivers - Chemical Entities" and Prologue "PBPK/PBBM to inform the Bioequivalence Safe Space, Food Effects, and pH-mediated DDIs" at the American Association of Pharmaceutical Scientists (AAPS) PharmSci 360 Annual Meeting in Boston, MA, on October 16-19, 2022, and are summarized here.

In Silico Clinical Trials: Is It Possible?

Modeling and simulation (M&S), including in silico (clinical)trials, helps accelerate drug research and development and reduce costs and have coined the term "model-informed drug development (MIDD)." Data-driven, inferential approaches are now becoming increasingly complemented by emerging complex physiologically and knowledge-based disease (and drug) models, but differ in setup, bottlenecks, data requirements, and applications (also reminiscent of the different scientific communities they arose from). At the same time, and within the MIDD landscape, regulators and drug developers start to embrace in silico trials as a potential tool to refine, reduce, and ultimately replace clinical trials. Effectively, silos between the historically distinct modeling approaches start to break down. Widespread adoption of in silico trials still needs more collaboration between different stakeholders and established precedence use cases in key applications, which is currently impeded by a shattered collection of tools and practices. In order to address these key challenges, efforts to establish best practice workflows need to be undertaken and new collaborative M&S tools devised, and an attempt to provide a coherent set of solutions is provided in this chapter. First, a dedicated workflow for in silico clinical trial (development) life cycle is provided, which takes up general ideas from the systems biology and quantitative systems pharmacology space and which implements specific steps toward regulatory qualification. Then, key characteristics of an in silico trial software platform implementation are given on the example of jinkō.ai (nova's end-to-end in silico clinical trial platform). Considering these enabling scientific and technological advances, future applications of in silico trials to refine, reduce, and replace clinical research are indicated, ranging from synthetic control strategies and digital twins, which overall shows promise to begin a new era of more efficient drug development.

Improving pharmacometrics analysis efficiency using DataCheQC: An interactive, Shiny-based app for quality control of pharmacometrics datasets.

DataCheQC is an interactive application based on the R Shiny framework developed for the purposes of performing quality control (QC) checks on pharmacometric datasets, and thereby supporting the implementation of model-informed drug development. Features include visual inspection of variables and data entries for errors and/or anomalies, and ensuring structural integrity through comparison with a dataset specification file. The app, which requires no programming knowledge to operate, allows the user to collect all findings into a summary report downloadable directly from the app itself. The source code for the app is freely available on GitHub under an open-source license (https://github.com/DotanOr/DataCheQC) and can also be accessed online (https://dotanor.shinyapps.io/DataCheQC/).

Translation of Monoclonal Antibodies Pharmacokinetics from Animal to Human Using Physiologically Based Modeling in Open Systems Pharmacology (OSP) Suite: A Retrospective Analysis of Bevacizumab.

Interspecies translation of monoclonal antibodies (mAbs) pharmacokinetics (PK) in presence of target-mediated drug disposition (TMDD) is particularly challenging. Incorporation of TMDD in physiologically based PK (PBPK) modeling is recent and needs to be consolidated and generalized to provide better prediction of TMDD regarding inter-species translation during preclinical and clinical development steps of mAbs. The objective of this study was to develop a generic PBPK translational approach for mAbs using the open-source software (PK-Sim® and Mobi®). The translation of bevacizumab based on data in non-human primates (NHP), healthy volunteers (HV), and cancer patients was used as a case example for model demonstration purpose. A PBPK model for bevacizumab concentration-time data was developed using data from literature and the Open Systems Pharmacology (OSP) Suite version 10. PK-sim® was used to build the linear part of bevacizumab PK (mainly FcRn-mediated), whereas MoBi® was used to develop the target-mediated part. The model was first developed for NHP and used for a priori PK prediction in HV. Then, the refined model obtained in HV was used for a priori prediction in cancer patients. A priori predictions were within 2-fold prediction error (predicted/observed) for both area under the concentration-time curve (AUC) and maximum concentration (Cmax) and all the predicted concentrations were within 2-fold average fold error (AFE) and average absolute fold error (AAFE). Sensitivity analysis showed that FcRn-mediated distribution and elimination processes must be accounted for at all mAb concentration levels, whereas the lower the mAb concentration, the more significant the target-mediated elimination. This project is the first step to generalize the full PBPK translational approach in Model-Informed Drug Development (MIDD) of mAbs using OSP Suite.

Model-Informed Drug Development: Steps Toward Harmonized Guidance.

Model-Informed Drug Development: Steps Toward Harmonized Guidance.

Integration of artificial neural network and physiologically based biopharmaceutic models in the development of sustained-release formulations.

Model-informed drug development is an important area recognized by regulatory authorities and is gaining increasing interest from the generic drug industry. Physiologically based biopharmaceutics modeling (PBBM) is a valuable tool to support drug development and bioequivalence assessments. This study aimed to utilize an artificial neural network (ANN) with a multilayer perceptron (MLP) model to develop a sustained-release matrix tablet of metformin HCl 500mg, and to test the likelihood of the prototype formulation being bioequivalent to Glucophage® XR, using PBBM modeling and virtual bioequivalence (vBE). The ANN with MLP model was used to simultaneously optimize 735 formulations to determine the optimal formulation for Glucophage® XR release. The optimized formulation was evaluated and compared to Glucophage® XR using PBBM modeling and vBE. The optimized formulation consisted of 228mg of hydroxypropyl methylcellulose (HPMC) and 151mg of PVP, and exhibited an observed release rate of 42% at 1h, 47% at 2h, 55% at 4h, and 58% at 8h. The PBBM modeling was effective in assessing the bioequivalence of two formulations of metformin, and the vBE evaluation demonstrated the utility and relevance of translational modeling for bioequivalence assessments. The study demonstrated the effectiveness of using PBBM modeling and model-informed drug development methodologies, such as ANN and MLP, to optimize drug formulations and evaluate bioequivalence. These tools can be utilized by the generic drug industry to support drug development and biopharmaceutics assessments.

Clinical Pharmacology Applications of Real-World Data and Real-World Evidence in Drug Development and Approval-An Industry Perspective.

Since the 21st Century Cures Act was signed into law in 2016, real-world data (RWD) and real-world evidence (RWE) have attracted great interest from the healthcare ecosystem globally. The potential and capability of RWD/RWE to inform regulatory decisions and clinical drug development have been extensively reviewed and discussed in the literature. However, a comprehensive review of current applications of RWD/RWE in clinical pharmacology, particularly from an industry perspective, is needed to inspire new insights and identify potential future opportunities for clinical pharmacologists to utilize RWD/RWE to address key drug development questions. In this paper, we review the RWD/RWE applications relevant to clinical pharmacology based on recent publications from member companies in the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) RWD Working Group, and discuss the future direction of RWE utilization from a clinical pharmacology perspective. A comprehensive review of RWD/RWE use cases is provided and discussed in the following categories of application: drug-drug interaction assessments, dose recommendation for patients with organ impairment, pediatric plan development and study design, model-informed drug development (e.g., disease progression modeling), prognostic and predictive biomarkers/factors identification, regulatory decisions support (e.g., label expansion), and synthetic/external control generation for rare diseases. Additionally, we describe and discuss common sources of RWD to help guide appropriate data selection to address questions pertaining to clinical pharmacology in drug development and regulatory decision making.

Model-informed drug development of autologous CAR-T cell therapy: Strategies to optimize CAR-T cell exposure leveraging cell kinetic/dynamic modeling.

Autologous Chimeric antigen receptor (CAR-T) cell therapy has been highly successful in the treatment of aggressive hematological malignancies and is also being evaluated for the treatment of solid tumors as well as other therapeutic areas. A challenge, however, is that up to 60% of patients do not sustain a long-term response. Low CAR-T cell exposure has been suggested as an underlying factor for a poor prognosis. CAR-T cell therapy is a novel therapeutic modality with unique kinetic and dynamic properties. Importantly, "clear" dose-exposure relationships do not seem to exist for any of the currently approved CAR-T cell products. In other words, dose increases have not led to a commensurate increase in the measurable in vivo frequency of transferred CAR-T cells. Therefore, alternative approaches beyond dose titration are needed to optimize CAR-T cell exposure. In this paper, we provide examples of actionable variables - design elements in CAR-T cell discovery, development, and clinical practice, which can be modified to optimize autologous CAR-T cell exposure. Most of these actionable variables can be assessed throughout the various stages of discovery and development as part of a well-informed research and development program. Model-informed drug development approaches can enable such study and program design choices from discovery through to clinical practice and can be an important contributor to cell therapy effectiveness and efficiency.

Model-Informed Approaches and Innovative Clinical Trial Design for Adeno-Associated Viral Vector-Based Gene Therapy Product Development: A White Paper.

The promise of viral vector-based gene therapy (GT) as a transformative paradigm for treating severely debilitating and life-threatening diseases is slowly coming to fruition with the recent approval of several drug products. However, they have a unique mechanism of action often necessitating a tortuous clinical development plan. Expertise in such complex therapeutic modality is still fairly limited in this emerging class of adeno-associated virus (AAV) vector-based gene therapies. Because of the irreversible mode of action and incomplete understanding of genotype-phenotype relationship and disease progression in rare diseases careful considerations should be given to GT product's benefit-risk profile. In particular, special attention needs to be paid to safe dose selection, reliable dose exposure response (including clinically relevant endpoints), or creative approaches in study design targeting small patient populations during clinical development. We believe that quantitative tools encompassed within model-informed drug development (MIDD) framework fits quite well in the development of such novel therapies, as they enable us to benefit from the totality of data approach in order to support dose selection as well as optimize clinical trial designs, end point selection, and patient enrichment. In this thought leadership paper, we provide our collective experiences, identify challenges, and suggest areas of improvement in applications of modeling and innovative trial design in development of AAV-based GT products and reflect on the challenges and opportunities for incorporating MIDD tools and more in rational development of these products.

Modelling Based Approaches to Support Generic Drug Regulatory Submissions-Practical Considerations and Case Studies.

Model informed drug development (MiDD) is useful to predict in vivo exposure of drugs during various stages of the drug development process. This approach employs a variety of quantitative tools to assess the risks during the drug development process. One important tool in the MiDD tool kit is the Physiologically Based Pharmacokinetic Modelling (PBPK). This tool is extensively used to reduce the development cost and to accelerate the access of medicines to the patients. In this work, we provide an overview of PBPK modelling approaches in the generic drug development process, with a special emphasis on the bio-waiver applications. We describe herein approaches and common pitfalls while submitting model based justifications as a response to the regulatory deficiencies during the generic drug development process. With some in-house case studies, we have attempted to provide a clear path for PBPK model based justifications for bio-waivers. With this review, the gap between theoretical knowledge and practical application of modelling and simulation tools for generic drug product development could be potentially reduced.

Modeling tumor size dynamics based on real-world electronic health records and image data in advanced melanoma patients receiving immunotherapy.

The development of immune checkpoint inhibitors (ICIs) has revolutionized cancer therapy but only a fraction of patients benefits from this therapy. Model-informed drug development can be used to assess prognostic and predictive clinical factors or biomarkers associated with treatment response. Most pharmacometric models have thus far been developed using data from randomized clinical trials, and further studies are needed to translate their findings into the real-world setting. We developed a tumor growth inhibition model based on real-world clinical and imaging data in a population of 91 advanced melanoma patients receiving ICIs (i.e., ipilimumab, nivolumab, and pembrolizumab). Drug effect was modeled as an ON/OFF treatment effect, with a tumor killing rate constant identical for the three drugs. Significant and clinically relevant covariate effects of albumin, neutrophil to lymphocyte ratio, and Eastern Cooperative Oncology Group (ECOG) performance status were identified on the baseline tumor volume parameter, as well as NRAS mutation on tumor growth rate constant using standard pharmacometric approaches. In a population subgroup (n = 38), we had the opportunity to conduct an exploratory analysis of image-based covariates (i.e., radiomics features), by combining machine learning and conventional pharmacometric covariate selection approaches. Overall, we demonstrated an innovative pipeline for longitudinal analyses of clinical and imaging RWD with a high-dimensional covariate selection method that enabled the identification of factors associated with tumor dynamics. This study also provides a proof of concept for using radiomics features as model covariates.

Pharmacokinetic analysis across studies to drive knowledge-integration: A tutorial on individual patient data meta-analysis (IPDMA).

Answering challenging questions in drug development sometimes requires pharmacokinetic (PK) data analysis across different studies, for example, to characterize PKs across diverse regions or populations, or to increase statistical power for subpopulations by combining smaller size trials. Given the growing interest in data sharing and advanced computational methods, knowledge integration based on multiple data sources is increasingly applied in the context of model-informed drug discovery and development. A powerful analysis method is the individual patient data meta-analysis (IPDMA), leveraging systematic review of databases and literature, with the most detailed data type of the individual patient, and quantitative modeling of the PK processes, including capturing heterogeneity of variance between studies. The methodology that should be used in IPDMA in the context of population PK analysis is summarized in this tutorial, highlighting areas of special attention compared to standard PK modeling, including hierarchical nested variability terms for interstudy variability, and handling between-assay differences in limits of quantification within a single analysis. This tutorial is intended for any pharmacological modeler who is interested in performing an integrated analysis of PK data across different studies in a systematic and thorough manner, to answer questions that transcend individual primary studies.

Challenges, approaches and enablers: effectively triangulating towards dose selection in pediatric rare diseases.

Dose selection is an integral part of a molecule's journey to become medicine. On top of typical challenges faced in dose selection for more common diseases, pediatric rare disease has additional unique challenges due to the combination of 'rare' and 'pediatric' populations. Using the central theme of maximizing 'relevant' information to overcome information paucity, dose selection strategy in pediatric rare diseases is discussed using a triangulation concept involving challenges, approaches and very importantly, enablers. Using actual examples, unique scenarios are discussed where specific enablers allowed certain approaches to be used to overcome the challenges. The continued need for model-informed drug development is also discussed using examples of where modeling and simulation tools have been successfully used in bridging available information to select pediatric doses in rare disease. Additionally, challenges with translation and associated dose selection of new modalities such as gene therapy in rare diseases are examined with the lens of continuous learning and knowledge development that will enable pediatric dose selection of these modalities with confidence.