Automation in pharmaceutical sector by implementation of artificial intelligence platform: a way forward
DOI:
https://doi.org/10.18203/2319-2003.ijbcp20212387Keywords:
Artificial intelligence, Clinical research, Pharmacovigilance, Medical affairs, Pharmaceuticals marketingAbstract
Worldwide, there are technological advances that swift automation in several aspects of the pharmaceutical industry such as pharmacovigilance, clinical research, medical affairs, and marketing. Innovative technology like artificial intelligence (AI) emphasizes the massive use of the internet for drug development, drug safety, data analytics, communication marketing, and customer engagement to achieve the goal of pharmaceuticals and patient-centric healthcare. Presently, escalating the number of individual case safety reports (ICSRs) necessitate the support of AI in the transformation of drug safety professional. AI can be transformed and evolve the clinical trial process from the conventional method alongside benefited the cutting cost, enhancing the trial quality, and alleviate trial time by almost half. Today, AI may be efficiently implemented to lower the cost of medical information requests, besides the online chatbots to communicate with health care professionals (HCPs) and consumers. There are numerous forthcoming uses of AI which need to be executed for renovation in the field of pharmaceuticals.
References
Schmider J, Kumar K, LaForest C, Swankoski B, Naim K, Caubel P. Innovation in Pharmacovigilance: Use of Artificial Intelligence in Adverse Event Case Processing. Clin Pharmacol Therap. 2018;105(4):954-61.
Cabitza F, Rasoini R, Gensini G. Unintended Consequences of Machine Learning in Medicine. JAMA. 2017;318(6):517.
Nayak V, Khan M, Shukla B, Chaturvedi P. Artificial intelligence in clinical research. Int J Clin Trials. 2016;3(4):187-93.
Hoodbhoy Z, Hasan B, Siddiqui K. Does artificial intelligence have any role in healthcare in low resource settings? J Med Art Intel. 2019;2:13-13.
Healthcare in 2065. 2021. Available at: https://www2.deloitte.com/content/dam/Deloitte/sg/Documents/risk/sg-risk-healthcare-2065-noexp.pdf. Accessed on 24 January 2021.
Danysz K, Cicirello S, Mingle E, Assuncao B, Tetarenko N, Mockute R, et al. Artificial Intelligence and the Future of the Drug Safety Professional. Drug Safety. 2018;42(4):491-7.
Mockute R, Desai S, Perera S, Assuncao B, Danysz K, Tetarenko N, et al. Artificial Intelligence Within Pharmacovigilance: A Means to Identify Cognitive Services and the Framework for Their Validation. Pharm Med. 2019;33(2):109-20.
Chan H, Hadjiiski L, Samala R. Computer‐aided diagnosis in the era of deep learning. Med Physics. 2020;47(5).
Frey C, Osborne M. The future of employment: How susceptible are jobs to computerization? 2021. Available at: https://www.oxfordmartin.ox.ac. uk/publications/the-future-of-employment/. Accessed on 24 January 2021.
Marr B. Instead of Destroying Jobs Artificial Intelligence (AI) Is Creating New Jobs In 4 Out Of 5 Companies. Forbes. 2021. Available at: https://www.forbes.com/sites/bernardmarr/2017/10/12/instead-of-destroying-jobs-artificial-intelligence-ai-is-creating-new-jobs-in-4-out-of-5-companies/. Accessed on 24 January 2021.
WHO Policy Perspectives on Medicines. Looking at the Pharmacovigilance: ensuring the safe use of medicines. Geneva: World Health Organization. Available at: http://apps.who.int/medic inedo cs/pdf/s6164 e/s6164 e.pdf. Accessed on 15 December 2020.
Basile A, Yahi A, Tatonetti N. Artificial Intelligence for Drug Toxicity and Safety. Trends Pharmacol Sci. 2019;40(9):624-35.
Sarker A, Ginn R, Nikfarjam A, O’Connor K, Smith K, Jayaraman S, et al. Utilizing social media data for pharmacovigilance: A review. 2015;54:202-12.
Caster O, Dietrich J, Kürzinger M, Lerch M, Maskell S, Norén G et al. Assessment of the Utility of Social Media for Broad-Ranging Statistical Signal Detection in Pharmacovigilance: Results from the WEB-RADR Project. Drug Safety. 2018;41(12):1355-69.
Yang C, Yang H, Jiang L. Post marketing Drug Safety Surveillance Using Publicly Available Health-Consumer-Contributed Content in Social Media. 2014;5(1):1-21.
Wang X, Hripcsak G, Markatou M, Friedman C. Active Computerized Pharmacovigilance Using Natural Language Processing, Statistics, and Electronic Health Records: A Feasibility Study. J Am Med Informat Assoc. 2009;16(3):328-37.
Luo Y, Thompson W, Herr T, Zeng Z, Berendsen M, Jonnalagadda S et al. Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review. Drug Safety. 2017;40(11):1075-89.
UMC WHO. Available at: https://www.who-umc.org/about-us/who-we-are/. Accessed on 11 October 2018.
Mak KK, Pichika MR. Artificial intelligence in drug development: Present status and future prospects. Drug Discov Today. 2019;24:773-80.
Duch W, Swaminathan K, Meller J. Artificial intelligence approaches for rational drug design and discovery. Curr Pharm Des. 2007;13(14):1497-508.
Blasiak A, Khong J, Kee T. CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence. SLAS Technol. 2020;25(2):95-105.
Mishra V. Artificial intelligence: the beginning of a new era in pharmacy profession. Asian J Pharm. 2018;12(2).
Sellwood MA. Artificial intelligence in drug discovery. Future Sci. 2018; 2025-8.
Álvarez-Machancoses Ó, Fernández-Martínez JL. Using artificial intelligence methods to speed up drug discovery. Expert Opin Drug Disc. 2019;14:769-77.
Fleming N. How artificial intelligence is changing drug discovery. Nature. 2018;557:55.
Harrer S. Artificial intelligence for clinical trial design. Trends Pharmacol Sci. 2019;40:577-91.
Hay M. Clinical development success rates for investigational drugs. Nature Biotechnol. 2014;32:40-51.
Fogel DB. Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: a review. Contemp Clin Trials Communic. 2018;11:156-64
Angus Derek C. Randomized Clinical Trials of Artificial Intelligence. JAMA. 2020;323(11):1043.
Parikh RB, Teeple S, Navathe AS. Addressing bias in artificial intelligence in health care. JAMA. 2019;322:2377.
Emanuel EJ, Wachter RM. Artificial intelligence in health care: will the value match the hype? JAMA. 2019;321:2281‐2.
Nayak VS, Khan MS, Shukla BK, Chaturvedi PR. Artificial intelligence in clinical research. Int J Clin Trials. 2016;3(4):187‐93.
Woo M. An AI boost for clinical trials. Nature. 2019;573(7775):100‐2.
Taylor K, Properzi F, Cruz MJ Intelligent clinical trials. Deloitte. 2020. Available at: https://www2.deloitte.com/us/en/insights/industry/life-sciences/artificial-intelligence-in-clinical-trials.html. Accessed on 15 April 2020.
From Aspiration to Action: Embracing a Customer-Centric Mindset in Medical Affairs Through Digital Transformation. Available at: http://engage. diaglobal.org/Indegene-Whitepaper-2020.html. Available at: 25 January 2021.
Hedwig M, Friesdorf M, Goryunov Y, Niedermann F. Omnichannel consumer interactions – a payer perspective. Available at: https://www.mckinsey. com/industries/ health care-systems-and-services/our-insights/omnichannel-consumer-interactions-a-payer-perspective. Accessed on 20 May 2020.
An Experience Insights Consultancy. Available at: https://gostonemantel.com/. 2020. Accessed on 20 May 2020.
Duncan E, Fanderl H, Maechler N, Neher K. Creating value through transforming customer journeys. Winter 2016. Available at: https://www.mckinsey. com/business-functions/marketing-and-sales/our-insights/customer-experience-creating-value-through-transforming-customer-journeys. Accessed on 20 May 2020.
Kalafatis SP, Tsogas MH, Blankson C. (2000) Positioning strategies in business markets. J Bus Industr Market. 2000;15(6).
Jalkala AM, Keränen J. Brand positioning strategies for industrial firms providing customer solutions. J Bus Industr Market. 2014;29(3).
Ding M, Eliashberg J, Stremersch S. Innovation and Marketing in the Pharmaceutical Industry, Springer Nature. 2014th edition. 2016.
Dou W. Brand positioning strategy using search engine marketing. Mis Quarterly. 2010;261-79.
Toker D. A decision model for pharmaceutical marketing and a case study in Turkey. Ekonomska Istraživanja 2013;26:101-14.
Singh J. Sales profession and professionals in the age of digitization and artificial intelligence technologies: concepts, priorities, and questions. J Personal Selling Sales Management 2019;39:2-22.
Mahajan KN, Kumar A. Business intelligent smart sales prediction analysis for pharmaceutical distribution and proposed generic model. Int J Comp Sci Informat Technol. 2017;8:407-12.
Ramesh A. Artificial intelligence in medicine. Ann Royal Coll Surg Engl. 2004;86, 334-8.
Miles J, Walker A. The potential application of artificial intelligence in transport. In IEE Proceedings-Intelligent Transport Systems 2006;153:183-98.
Yang Y, Siau K. A Qualitative Research on Marketing and Sales in the Artificial Intelligence Age, MWAIS. 2018.
Wirtz BW. Artificial intelligence and the public sector—applications and challenges. Int J Public Admin. 2019;42:596-615.
Smith RG, Farquhar A. The road ahead for knowledge management: an AI perspective. AI Magazine. 2000;21:17.
Lamberti MJ. A study on the application and use of artificial intelligence to support drug development. Clin Therap. 2019;41:1414-26.
Research and markets. Global Growth Insight - Role of AI in the Pharmaceutical Industry 2018-2022: Exploring Key Investment Trends, Companies-to-Action, and Growth Opportunities for AI in the Pharmaceutical Industry, Research and Markets. 2019.
Madariaga A, Kasherman L, Karakasis K, Degendorfer P, Heesters A, Xu W, et al. Optimizing clinical research procedures in public health emergencies. Med Res Rev. 2020.
