Artificial intelligence in the pharmaceutical sector: current scene and future prospect

Kalyane, Dnyaneshwar; Sanap, Gaurav; Paul, Debleena; Shenoy, Snehal; Anup, Neelima; Polaka, Suryanarayana; Tambe, Vishakha; Tekade, Rakesh K.

doi:10.1016/b978-0-12-814455-8.00003-7

Cited by 27 publications

(14 citation statements)

References 80 publications

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“…The Artificial Neural Network (ANN) depicts the cluster of interconnected and systematic components with computational capacity which imitate how electrical impulses are transmitted in the human brain by using "perceptons" that are symmetrical to actual human neurons. ANNs come in a variety of forms, including MLP networks, RNNs, and CNNs, which may be trained using supervised or unsupervised methods [12]. The IBM Watson supercomputer is one of the technologies created using AI (IBM, New York, USA).…”

Section: Ai In Pharmaceutical Industrymentioning

confidence: 99%

Artificial Intelligence for Computer-Aided Drug Discovery

Kate,

Seth,

Singh

et al. 2023

Drug Res (Stuttg)

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The continuous implementation of Artificial Intelligence (AI) in multiple scientific domains and the rapid advancement in computer software and hardware, along with other parameters, have rapidly fuelled this development. The technology can contribute effectively in solving many challenges and constraints in the traditional development of the drug. Traditionally, large-scale chemical libraries are screened to find one promising medicine. In recent years, more reasonable structure-based drug design approaches have avoided the first screening phases while still requiring chemists to design, synthesize, and test a wide range of compounds to produce possible novel medications. The process of turning a promising chemical into a medicinal candidate can be expensive and time-consuming. Additionally, a new medication candidate may still fail in clinical trials even after demonstrating promise in laboratory research. In fact, less than 10% of medication candidates that undergo Phase I trials really reach the market. As a consequence, the unmatched data processing power of AI systems may expedite and enhance the drug development process in four different ways: by opening up links to novel biological systems, superior or distinctive chemistry, greater success rates, and faster and less expensive innovation trials. Since these technologies may be used to address a variety of discovery scenarios and biological targets, it is essential to comprehend and distinguish between use cases. As a result, we have emphasized how AI may be used in a variety of areas of the pharmaceutical sciences, including in-depth opportunities for drug research and development.

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Section: Ai In Pharmaceutical Industrymentioning

confidence: 99%

Artificial Intelligence for Computer-Aided Drug Discovery

Kate,

Seth,

Singh

et al. 2023

Drug Res (Stuttg)

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“…Previous works [29][30][31][32][33][34][35] IoT: Internet of Things; 3D: three-dimensional outlining an effective risk communication plan (C29) that is audience focused and combining the elements of strategic planning and public engagement is essential in an organization's risk communication process. The internal and external audiences in risk communication, thus, involve communication between regulators and industry (C30), communication between industry and the patient (C31), and internal communication (C32).…”

Section: Risk Communicationmentioning

confidence: 99%

“…The application of artificial intelligence has been adopted throughout a product’s lifecycle, which includes drug discovery, manufacturing, quality control, and quality assurance. 29 Meanwhile, Liu et al 25 demonstrated machine learning’s role in detecting risks associated with medications. 30 With the digitization of manufacturing processes, O’Donovan et al 26 and Hernandez and Zhang 27 discussed the role of big data analysis (C39) in predictive analytics in identifying risks.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Liberati et al 24 also identified the role of Internet of Things (IoT) (C41) in manufacturers to optimize factory operations, product quality, and the manufacturing logistics. 29 In addition, the application of three-dimensional (3D) printing (C43) in pharmaceutical manufacturing assists in risk management. Through the utilization of 3D printing, patient safety can be achieved via the production of personalized medicine.…”

Section: Literature Reviewmentioning

confidence: 99%

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Risk control drives risk assessment and risk review: A cause and effect model of pharmaceutical drug recall on patient safety

Lin

Hertig

2023

The Journal of Medicine Access

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Background: Pharmaceutical drug recall is a relentless issue that is composed of multidimensional criteria. The distinct criteria that contributed to drug recalls have been identified in previous literature; however, there is limited information regarding the causal relationships between each criterion. Highlighting key influential aspects and criteria of pharmaceutical drug recall is critical in addressing this ongoing issue and promoting patient safety. Objective: The objective of this study is as follows: (1) identify critical criteria of pharmaceutical drug recalls for improvements, (2) determine the interrelationships among the criteria, and (3) define the causal relationships of pharmaceutical drug recall and provide theoretical insights and practice recommendations to minimize risks associated with pharmaceutical recalls and maximize patient safety. Design: This study proposes five aspects and 42 criteria to identify the impact of pharmaceutical drug recalls on patient safety by evaluating the interrelationships between the criteria by employing the fuzzy decision-making trial and evaluation laboratory method. Methods: A group of 11 professionals across the pharmaceutical industry, hospitals, ambulatory care, regulatory authority, and community care settings were selected for interviews. Results: Risk control is the influencing aspect of pharmaceutical drug recalls that has the most substantial impact on risk assessment and risk review; it generates medium effects on risk communication and technology. Risk assessment, risk communication, and risk review demonstrated comparative weak interrelationships, while risk communication exhibits a weak unidirectional effect on risk review. Finally, risk assessment exerts a weak influence on technology application and development. Product contamination, product subpotent or superpotent, injury to patients, product not sterile or impure, and system detectability of hazards have the strongest influence in the causal group of pharmaceutical drug recalls. Conclusion: The study shows that risk control drives risk assessment and risk review in the pharmaceutical industry manufacturing process. To achieve patient safety, this study suggests focusing on risk control strategies, as this aspect displays the most substantial effect on influencing other critical risk management aspects such as risk assessment and risk review.

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“…These expenditures begin with the scanning of millions of components during the initial phases of research and development (R&D) and conclude with high-cost clinical trials that often yield unpredictable outcomes. AI has the potential to be the solution to longstanding challenges in the industry, including issues related to time and the costs associated with drug development (Kalyane et al, 2020). AI can enhance the effectiveness of drug development processes, fostering collaboration between major players in the pharmaceutical industry and companies specializing in AI-driven drug discovery (Mak and Pichika, 2019).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in the Pharmaceutical Sector of India: Future Prospects and Challenges

Kimta,

Dogra

2024

Preprint

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The pharmaceutical sector in India has emerged as a global leader, contributing significantly to the production of affordable generic drugs and vaccines. Despite its success, the industry faces challenges from early research and development stages to market deployment. This research paper explores the future prospects and challenges of integrating Artificial Intelligence (AI) into the Indian pharmaceutical sector. Methodologically, the research relies on secondary data from 2011 to 2019, collected from various published sources, primarily the Reserve Bank of India. The evaluation of performance was conducted using compound annual growth rate. AI has the potential to address longstanding issues, enhance operational efficiency, and reduce costs associated with drug discovery and development. Successful integration requires collaborative efforts from the public and private sectors, emphasizing resource allocation for education, infrastructure, regulatory reforms, and workforce development. The incorporation of AI presents a transformative opportunity for the Indian pharmaceutical sector, offering advancements in drug development, manufacturing, and quality control. Despite the promising prospects, the paper acknowledges challenges in implementing AI, including financial limitations, workforce competency, data security concerns, and regulatory complexities. The study emphasizes the need for strategic collaboration to ensure responsible and effective utilization of AI in the pharmaceutical industry.

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Artificial intelligence in the pharmaceutical sector: current scene and future prospect

Cited by 27 publications

References 80 publications

Artificial Intelligence for Computer-Aided Drug Discovery

Artificial Intelligence for Computer-Aided Drug Discovery

Risk control drives risk assessment and risk review: A cause and effect model of pharmaceutical drug recall on patient safety

Artificial Intelligence in the Pharmaceutical Sector of India: Future Prospects and Challenges

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