Getting the Dose Right in Drug Development for Rare Diseases: Barriers and Enablers.

In the relentless pursuit of optimizing drug development, the intricate process of determining the ideal dosage unfolds. This involves "dose-finding" studies, crucial for providing insights into subsequent registration trials. However, the challenges intensify when tackling rare diseases. The complexity arises from poorly understood pathophysiologies, scarcity of appropriate animal models, and limited natural history understanding. The inherent heterogeneity, coupled with challenges in defining clinical end points, poses substantial challenges, hindering the utility of available data. The small affected population, low disease awareness, and restricted healthcare access compound the difficulty in conducting dose-finding studies. This white paper delves into critical dose selection aspects, focusing on key therapeutic areas, such as oncology, neurology, hepatology, metabolic rare diseases. It also explores dose selection challenges posed by pediatric rare diseases as well as novel modalities, including enzyme replacement therapies, cell and gene therapies, and oligonucleotides. Several examples emphasize the pivotal role of clinical pharmacology in navigating the complexities associated with these diseases and emerging treatment modalities.

New Horizons of Model Informed Drug Development in Rare Diseases Drug Development.

Model-informed approaches provide a quantitative framework to integrate all available nonclinical and clinical data, thus furnishing a totality of evidence approach to drug development and regulatory evaluation. Maximizing the use of all available data and information about the drug enables a more robust characterization of the risk-benefit profile and reduces uncertainty in both technical and regulatory success. This offers the potential to transform rare diseases drug development, where conducting large well-controlled clinical trials is impractical and/or unethical due to a small patient population, a significant portion of which could be children. Additionally, the totality of evidence generated by model-informed approaches can provide confirmatory evidence for regulatory approval without the need for additional clinical data. In the article, applications of novel quantitative approaches such as quantitative systems pharmacology, disease progression modeling, artificial intelligence, machine learning, modeling of real-world data using model-based meta-analysis and strategies such as external control and patient-reported outcomes as well as clinical trial simulations to optimize trials and sample collection are discussed. Specific case studies of these modeling approaches in rare diseases are provided to showcase applications in drug development and regulatory review. Finally, perspectives are shared on the future state of these modeling approaches in rare diseases drug development along with challenges and opportunities for incorporating such tools in the rational development of drug products.

Gemcitabine and Vinorelbine (GemVin) Regimen.

The complexity of cancer chemotherapy requires pharmacists be familiar with the complicated regimens and highly toxic agents used. This column reviews various issues related to preparation, dispensing, and administration of antineoplastic therapy, and the agents, both commercially available and investigational, used to treat malignant diseases. Questions or suggestions for topics should be addressed to Dominic A. Solimando, Jr, President, Oncology Pharmacy Services, Inc., 4201 Wilson Blvd #110-545, Arlington, VA 22203, e-mail: OncRxSvc@comcast.net; or J. Aubrey Waddell, Professor, University of Tennessee College of Pharmacy; Oncology Pharmacist, Pharmacy Department, Blount Memorial Hospital, 907 E. Lamar Alexander Parkway, Maryville, TN 37804, e-mail: waddfour@charter.net.

Application of pharmacokinetics-pharmacodynamics/clinical response modeling and simulation for biologics drug development.

Biologics, specifically monoclonal antibody (mAb) drugs, have unique pharmacokinetic (PK) and pharmacodynamic (PD) characteristics as opposed to small molecules. Under the paradigm of model-based drug development, PK-PD/clinical response models offer critical insight in guiding biologics development at various stages. On the basis of the molecular structure and corresponding properties of biologics, typical mechanism-based [target-mediated drug disposition (TMDD)], physiologically based PK, PK-PD, and dose-response meta-analysis models are summarized. Examples of using TMDD, PK-PD, and meta-analysis in helping starting dose determination in first-in-human studies and dosing regimen optimization in phase II/III trials are discussed. Instead of covering the entirety of model-based biologics development, this review focuses on the guiding principles and the core mathematical descriptions underlying the PK or PK-PD models most used.

Evaluation of structural models to describe the effect of placebo upon the time course of major depressive disorder.

Major depressive disorder (MDD) is the leading cause of disability in many countries. Designing and evaluating clinical trials of antidepressants is difficult due to the pronounced and variable placebo response which is poorly defined and may be affected by trial design. Approximately half of recent clinical trials of commonly used antidepressants failed to show statistical superiority for the drug over placebo, which is partly attributable to a marked placebo response. These failures suggest the need for new tools to evaluate placebo response and drug effect in depression, as well as to help design more informative clinical trials. Disease progression modeling is a tool that has been employed for such evaluations and several models have been proposed to describe MDD. Placebo data from three clinical depression trials were used to evaluate three published models: the inverse Bateman (IBM), indirect response (IDR) and transit (TM) models. Each model was used to describe Hamilton Rating Scale for major depression (HAMD) data and results were evaluated. The IBM model had several deficiencies, making it unsuitable. The IDR and TM models performed well on most evaluations and appear suitable. Comparing the IDR and TM models showed less clear distinctions, although overall the TM was found to be somewhat better than the IDR model. Model based evaluation can provide a useful tool for evaluating the time course of MDD and detecting drug effect. However, the models used should be robust, with well estimated parameters.