&NA; Previous estimates of drug development success rates rely on relatively small samples from databases curated by the pharmaceutical industry and are subject to potential selection biases. Using a sample of 406 038 entries of clinical trial data for over 21 143 compounds from January 1, 2000 to October 31, 2015, we estimate aggregate clinical trial success rates and durations. We also compute disaggregated estimates across several trial features including disease type, clinical phase, industry or academic sponsor, biomarker presence, lead indication status, and time. In several cases, our results differ significantly in detail from widely cited statistics. For example, oncology has a 3.4% success rate in our sample vs. 5.1% in prior studies. However, after declining to 1.7% in 2012, this rate has improved to 2.5% and 8.3% in 2014 and 2015, respectively. In addition, trials that use biomarkers in patient‐selection have higher overall success probabilities than trials without biomarkers.
Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep learning, and other advanced computational methods. These innovations have unlocked unprecedented opportunities for the acceleration of drug discovery and delivery, the optimization of treatment regimens, and the improvement of patient outcomes. AI is swiftly transforming the pharmaceutical industry, revolutionizing everything from drug development and discovery to personalized medicine, including target identification and validation, selection of excipients, prediction of the synthetic route, supply chain optimization, monitoring during continuous manufacturing processes, or predictive maintenance, among others. While the integration of AI promises to enhance efficiency, reduce costs, and improve both medicines and patient health, it also raises important questions from a regulatory point of view. In this review article, we will present a comprehensive overview of AI’s applications in the pharmaceutical industry, covering areas such as drug discovery, target optimization, personalized medicine, drug safety, and more. By analyzing current research trends and case studies, we aim to shed light on AI’s transformative impact on the pharmaceutical industry and its broader implications for healthcare.
The pharmaceutical industry in Indonesia has grown significantly, accounting for 27.8% of the ASEAN market share. However, despite this growth, the sector faces challenges, including intense competition, complex supply chain dynamics, and inefficiencies in production processes. This research investigates the factors contributing to the production decline and identifies non-value-added activities in the Vitamin C production line using Value Stream Mapping. The study applies Lean Manufacturing principles, supported by Root Cause Analysis, to propose improvement strategies that enhance line utilization and reduce waste. Data were collected through observation and interviews leading to the development of current and future state process models. The findings reveal that ineffective scheduling, prolonged changeover times, and labour-intensive documentation significantly reduce production efficiency. To address these issues, the study recommends the implementation of rapid testing technologies, electronic batch records, and a Laboratory Information Management System. These interventions are expected to result a 62.7% reduction in lead time and a 176% increase in production capacity. Financial analysis indicates strong economic feasibility, with a positive net present value and short payback period. This study concludes that Lean Manufacturing tools, when tailored to pharmaceutical operations, can drive substantial improvements in productivity, process flow, and operational resilience.
Irvin Tubon, Erick Cunalata , Goering Octavio Zambrano-Cárdenas
et al.
Background: Diabetes mellitus is a chronic disease affecting many people in the world. The main symptom of diabetes is high blood glucose levels (hyperglycemia), which triggers an imbalance in the body, producing secondary pathologies associated with oxidative stress generated by this metabolic disorder. Objective: This research evaluated the antioxidant and hypoglycemic capacity of Ambrosia arborescens, Buddleja incana, Aloysia citrodora, and Prunus serotina ethanolic and aqueous leaf extracts. Methods: The phytochemical profile of each plant species was characterized through qualitative tests to determine the presence or absence of metabolites such as alkaloids, phenols, triterpenes, and flavonoids. Quantitative determinations of total phenols and flavonoid content were also conducted. The free radical scavenging assay with 2,2-diphenyl-1picrylhydrazil (DPPH) evaluated the antioxidant capacity. The hypoglycemic capacity was performed by quantifying the inhibition capacity of α-amylase and α-glucosidase enzymes. Results: All extracts showed a high concentration of phenols and flavonoids. Likewise, all extracts exhibited enzymatic inhibition at different concentrations, with 500 µg/mL showing the highest inhibitory effect. Additionally, the ethanolic extract of A. arborescens demonstrated the most excellent hypoglycemic capacity among all the extracts analyzed. Conclusion: The results of this study can serve as a basis for future research focused on utilizing medicinal plants to develop pharmaceutical formulations as an alternative treatment for hyperglycemia associated with diabetes.
Food processing and manufacture, Pharmaceutical industry