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Analysis of the Real-Time Oncology Review (RTOR) Pilot Program for Approvals of New Molecular Entities.

ABSTRACT: The United States Food and Drug Administration (FDA) Oncology Center of Excellence (OCE) established the Real-Time Oncology Review (RTOR) pilot program in 2017 to streamline the review process for oncology drug applications with the applicant and the Agency agreeing upon a piecemeal strategy and timeline for module components. The Prescription Drug User Fee Act (PDUFA) review clock does not officially start until the final component is submitted. Participation requires careful planning of time and resources due to the multiple submissions and interactions with the FDA. Applicants must also meet certain criteria regarding the clinical trial design and development program to be eligible for RTOR. Publicly available databases (Drugs@FDA) and documents were searched for all RTOR applications, which revealed a total of 28 approved applications that participated from February 2018 to August 2020. Initial marketing applications were further reviewed to identify any potential advantages or limitations from participation in the pilot program. These four case studies demonstrated an individualized RTOR process reflecting the program's pilot status. The FDA approved 3 out of the 4 applications approximately three to four months before the PDUFA goal date. The time savings is not guaranteed as other parts of the review may influence the overall timeline. However, the optional biweekly teleconferences increased communication and collaboration between the applicant and the FDA. The full impact of RTOR on applications remains undetermined as the number of approved applications that have participated in the pilot program is still relatively small.

SUBMITTER: Feng C

PROVIDER: S-EPMC8081281 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The Biopharmaceutics Drug Disposition Classification System (BDDCS) was successfully employed for predicting drug-drug interactions (DDIs) with respect to drug metabolizing enzymes (DMEs), drug transporters and their interplay. The major assumption of BDDCS is that the extent of metabolism (EoM) predicts high versus low intestinal permeability rate, and vice versa, at least when uptake transporters or paracellular transport is not involved. We recently published a collection of over 900 marketed drugs classified for BDDCS. We suggest that a reliable model for predicting BDDCS class, integrated with in vitro assays, could anticipate disposition and potential DDIs of new molecular entities (NMEs). Here we describe a computational procedure for predicting BDDCS class from molecular structures. The model was trained on a set of 300 oral drugs, and validated on an external set of 379 oral drugs, using 17 descriptors calculated or derived from the VolSurf+ software. For each molecule, a probability of BDDCS class membership was given, based on predicted EoM, FDA solubility (FDAS) and their confidence scores. The accuracy in predicting FDAS was 78% in training and 77% in validation, while for EoM prediction the accuracy was 82% in training and 79% in external validation. The actual BDDCS class corresponded to the highest ranked calculated class for 55% of the validation molecules, and it was within the top two ranked more than 92% of the time. The unbalanced stratification of the data set did not affect the prediction, which showed highest accuracy in predicting classes 2 and 3 with respect to the most populated class 1. For class 4 drugs a general lack of predictability was observed. A linear discriminant analysis (LDA) confirming the degree of accuracy for the prediction of the different BDDCS classes is tied to the structure of the data set. This model could routinely be used in early drug discovery to prioritize in vitro tests for NMEs (e.g., affinity to transporters, intestinal metabolism, intestinal absorption and plasma protein binding). We further applied the BDDCS prediction model on a large set of medicinal chemistry compounds (over 30,000 chemicals). Based on this application, we suggest that solubility, and not permeability, is the major difference between NMEs and drugs. We anticipate that the forecast of BDDCS categories in early drug discovery may lead to a significant R&D cost reduction.

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Analysis of the Real-Time Oncology Review (RTOR) Pilot Program for Approvals of New Molecular Entities.

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