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Problem definition: To approve a novel drug therapy, the U.S. Food and Drug Administration (FDA) requires clinical trial evidence demonstrating efficacy with 2.5% statistical significance, although the agency often uses regulatory discretion when interpreting these standards. Factors including disease severity, prevalence, and availability of exist...
Contexts in source publication
Context 1
... drug approval process in the United States consists of a series of stages, beginning with the discovery of a new pharmaceutical compound and ending with the FDA deciding whether to grant marketing approval. Figure 1 provides a summary and average duration of each stage (PhRMA 2015). ...
Context 2
... scenario (i), we compute the trial completion rate based on the average trial duration by phase as reported in Online Appendix B, Table B3, and the phase-specific abandonment rate to match Thomas et al. (2016); thus, the rate of drugs entering NDA review is unchanged. Figure 10 shows that the objective function, and thus the optimal approval policy, in scenarios (i) and (ii) are virtually identical to the base model, suggesting that our earlier analysis is robust to structural and distributional assumptions in the prereview queueing model. ...
Context 3
... the remaining nine classes absorb fewer new drugs, decreasing the expected total number of approved effective drugs in the market because of eventual drug obsolescence. Given this market concentration, overall expected net benefits are lower than in our base case, yet the general shape of the objective function is largely unchanged, suggesting that the optimal policy α * only modestly changes (Figure 11). Of course, in reality, different drug classes may confer different health benefits. ...
Context 4
... we cannot link a drug's specific clinical trial pathway from Phase I to FDA review, we instead use average durations. We examine whether clinical trial durations are approximately exponentially distributed for use in the M/M/∞ queueing model ( Figure B1). Although Phase I trials are not perfectly exponentially distributed, the latter phases more closely satisfy this assumption, and they comprise the majority of total drug development time. ...
Citations
... [8] Because the entire drug discovery operation takes a long time, including pre and post-approval, the scientific community is pursuing groundbreaking methods to maintain robust drug discovery routes for SARS-CoV-2, which feel very aggressive, especially considering its respiratory effect. [9] These strategies include repurposing existing antimicrobial and antiviral drugs and utilizing computational tools to identify new compounds or phytochemicals. [10] It can be concluded that the in silico method is a useful tool that helps to speed up the process of a potential drug candidate's discovery, and boosts its further identification both in vitro and in vivo. ...
This study explores the capability of thiazoles as potent inhibitors of SARS‐CoV‐2 Mpro. Seventeen thiazoles (1–17) were screened for their linking affinity with the active site of SARS‐CoV‐2 Mpro and compared with the FDA‐recommended antiviral drugs, Remdesivir and Baricitinib. Density Functional Theory (DFT) calculations provided electronic and energetic properties of these ligands, shedding light on their stability and reactivity. Molecular docking analysis revealed that thiazole derivatives exhibited favorable linking affinities with various functional sites of SARS‐CoV‐2 proteins, including spike receptor‐linking zone, nucleocapsid protein N‐terminal RNA linking zone, and Mpro. Notably, compounds 3, 10, and 12 displayed the best interaction with 6LZG as compared to FDA‐approved antiviral drugs Remdesivir and Baricitinib, while compounds 1, 10, and 8 exhibited strong linking with 6 M3 M and also better than Remdesivir and Baricitinib. Additionally, compounds 3, 1, and 6 showed promising interactions with 6LU7 but only compound 3 performed better than Baricitinib. An ADME (Absorption, Distribution, Metabolism, and Excretion) study provided insights into the pharmacokinetics and drug‐likeness of these compounds, with all ligands demonstrating good physicochemical characteristics, lipophilicity, water solubility, pharmacokinetics, drug‐likeness, and medicinal chemistry attributes. The results suggest that these selected thiazole derivatives hold promise as potential candidates for further drug development.
... This assumption helped uncover simple criteria of collaboration based on the drug quality and development cost rate. With the same assumption, and incorporating disease-specific factors and obsolescence, Bravo et al. (2022) proposed a queuing framework to find an optimal drug approval policy with intuitive interpretations to help FDA's approval process. ...
Clinical testing accounts for almost 70% of the R &D expenditure in the pharmaceutical industry and most clinical trials experience considerable delays that result in extra costs. One of the major sources of these delays is the patient recruitment process. It is estimated that about 48% of the recruitment sites of a clinical trial fail to meet their deadlines due to slow recruitment rates and 11% do not recruit a single patient. Thus, determining the correct number of recruitment sites is paramount for more efficient supply chains in clinical trials. The calculation of the number of recruitment sites in clinical trials is based on factors such as the target number of patients, their estimated recruitment rates, and the associated operational costs. However, these calculations typically ignore the time needed for sites to be operational. In this paper, we analyze the impact of incorporating these site activation delays in determining the optimal number of clinical sites. Our study shows that ignoring these delays leads to overestimating the number of sites needed, thus resulting in an excess of expenditure. Consequently, the insights herein explained help quantify the impact of making better decisions in designing more efficient operations in clinical trials, both in terms of time and money.
... Several recent papers in the decision analysis and OM literature have examined different decision points in the drug approval process, beginning with clinical trial evaluation and regulatory approval. Bravo et al. (2022) ask whether the U.S. Food and Drug Administration's approval criteria-a statistical threshold (i.e., the probability of a type I error) used to assess drug noninferiority-should vary by disease. Using a queueing network model, they show that the optimal approval policy should depend on characteristics of the drug pipeline, as well as alternative therapies available on the market, to maximize expected net health benefits. ...
Health remains one of the most challenging realms for decision makers and policy making while critical for the well-being of humans, the stability of societies, and the development of economies. Decision making in this field ranges from medical doctors identifying the best treatments for patients, healthcare companies selecting the most promising drugs for development, healthcare providers deciding for adequate levels of resourcing, health regulators deciding whether to approve a new medicine or health technology, to regional and national health departments identifying how to increase the health security of regions and countries. In this positioning paper, and introduction to this Special Issue, we present the history, evolution, and trends of health decision analysis and suggest that these developments and news trends can be conceptualized as an emerging field of applied research for our discipline: Health Decision Analysis.
... Our work focuses on uncertainties related to the efficacy (incremental health benefits and costs) of a HT adoption decision relative to the cost of research, rather than on uncertainties in patient accrual, and models the cost of effort to obtain a given recruitment rate on an empirical basis. Bravo et al. (2018) ...
... For trials in a clinical area with lower rates of innovation, determining the population size with an infinite horizon discounted reward model makes sense. In clinical areas with rapid innovation, the model of Bravo et al. (2018) may be useful to set the population size. Importantly, the optimal sample size is proportional to the square root of the incidence (for a large incidence). ...
Health technology assessments often inform decisions made by public payers, such as the UK’s National Health Service, as they negotiate the pricing of companies’ new health technologies. A common assessment mechanism compares the incremental cost-effectiveness ratio (ICER) of the new health technology, relative to a standard of care, to a maximum threshold on the cost per quality-adjusted life year. In much research and practice, these assessments may not distinguish between cost-per-patient and negotiated price, effectively ignoring the value-based-pricing principle that better health outcomes merit higher prices. Other research makes this distinction, but it does not account for uncertainty in the ICER associated with clinical trial data that are limited in size and scope. This paper models the strategic behavior of a payer and a company as they price a new health technology, and it considers the use of conditional approval (CA) schemes whose post-marketing trials reduce ICER uncertainty before final pricing decisions are made. Analytical results suggest a very different view of the value-based pricing negotiations underlying these schemes: interim prices used during CA post-marketing trials should reflect cost-sharing for the CA scheme, not just cost-effectiveness goals for a treatment. Moreover, the types of caps on interim prices used by entities such as the UK Cancer Drugs Fund may hinder the development of new technologies and lead to suboptimal CA designs. We propose a new risk-sharing mechanism to remedy this. Numerical results, calibrated to approval data of an oncology drug, illustrate the issues in a practical setting.
This paper was accepted by Stefan Scholtes, healthcare management.
Funding: Financial support from the Mack Institute for Innovation Management at the Wharton School to the authors and the support of Dr. Simba Gill and Sabi Dau to the INSEAD Healthcare Management Initiative are gratefully acknowledged.
Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.03628 .
Motivation
Target discovery is a crucial step in drug development, as it directly affects the success rate of clinical trials. Knowledge graphs (KGs) offer unique advantages in processing complex biological data and inferring new relationships. Existing biomedical KGs primarily focus on tasks such as drug repositioning and drug-target interactions, leaving a gap in the construction of KGs tailored for target discovery.
Results
We established a comprehensive biomedical KG focusing on target discovery, termed TarKG, by integrating seven existing biomedical KGs, nine public databases, and traditional Chinese medicine knowledge databases. TarKG consists of 1,143,313 entities and 32,806,467 relations across 15 entity categories and 171 relation types, all centered around three core entity types: Disease, Gene, Compound. TarKG provides specialized knowledges for the core entities including chemical structures, protein sequences or text descriptions. By using different KG embedding algorithms, we assessed the knowledge completion capabilities of TarKG, particularly for disease-target link prediction. In case studies, we further examined TarKG’s ability to predict potential protein targets for Alzheimer’s disease (AD) and to identify diseases potentially associated with the metallo-deubiquitinase CSN5, using literature analysis for validation. Furthermore, we provided a user-friendly web server (https://tarkg.ddtmlab.org) that enables users to perform knowledge retrieval and relation inference using TarKG.
Availability
TarKG is accessible at https://tarkg.ddtmlab.org.
Supplementary information
Supplementary data are available at Bioinformatics online.
