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 Table of Contents  
REVIEW ARTICLE
Year : 2022  |  Volume : 18  |  Issue : 4  |  Page : 217-220

Need for artificial intelligence in pharmaceutical industry and its limitations


Department of Pharmacology, Rajasthan University of Health Sciences, College of Medical Sciences, Jaipur, Rajasthan, India

Date of Submission11-Jul-2022
Date of Decision10-Oct-2022
Date of Acceptance17-Oct-2022
Date of Web Publication27-Dec-2022

Correspondence Address:
Dr. Varun Pareek
K101 Plot 173-174 Kushmanda Apt., Kishan Nagar, Shyam Nagar, Jaipur - 302 019, Rajasthan
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jiag.jiag_33_22

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  Abstract 


Artificial intelligence (AI) is often being touted as the means to bring about the fourth industrial revolution and its role in almost all sectors of our society is almost certain. This brings about an urgent need for evaluating the benefits and limitations of AI and machine learning (ML) across various sectors. Pharmaceutical industry has pioneered in embracing the use of AI in all its core areas but the success as of now seems very limited. The major advantage of AI is that it reduces the time that is needed for drug development, and in turn, it reduces the costs that are associated with drug development, enhances the returns on investment, and may even cause a decrease in cost for the end user along with improved drug safety. Hence, in this article, we will review the scope and limitations of AI in the pharmaceutical industry along with the brief review of how AI/ML can impact geriatric health care.

Keywords: Artificial intelligence, clinical trial, drug design, drug discovery, electronic medical records, geriatric, machine learning


How to cite this article:
Pareek V, Sharma L, Kumar S, Sharma V. Need for artificial intelligence in pharmaceutical industry and its limitations. J Indian Acad Geriatr 2022;18:217-20

How to cite this URL:
Pareek V, Sharma L, Kumar S, Sharma V. Need for artificial intelligence in pharmaceutical industry and its limitations. J Indian Acad Geriatr [serial online] 2022 [cited 2023 Feb 8];18:217-20. Available from: http://www.jiag.com/text.asp?2022/18/4/217/365769




  Introduction Top


Artificial intelligence (AI) is the mimicking of human intelligence patterns and processes by machines and computer systems.

As a general rule, AI frameworks work by breaking down large amounts of specified training data for correlations and patterns,[1] and using these patterns to make forecasts about future states.

AI programming focuses on the following cognitive skills:

  1. Learning
  2. Reasoning
  3. Self-correction
  4. Planning
  5. Knowledge representation
  6. Automated decision-making.


Countless studies are being conducted worldwide on AI that will usher in the fourth Industrial Revolution [Figure 1]. A lot of cash is being pumped in to make a framework that can work indefinitely more productively and at a substantially less time than a typical individual. Be it an educational setup, a manufacturing firm, a government office, or a research firm, AI finds itself relevant in each field.
Figure 1: Representative image of the progress through industrial revolutions. (Source: https://theaseanpost. com/article/southeast-asia-and-fourth-industrial-revolution)

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Examples of AI include speech recognition,[2] language processing, self-driving cars, personalized social media feeds, and personalized web searches.


  Applications of Artificial Intelligence in Pharmacology and why it is Needed in Pharmacology Top


Drug development and designing is an expensive and lengthy process [Figure 2] and [Figure 3].[3] Some estimates state that it takes on an average of 10 years and approximately 2.6 billion USD to develop a single molecule.
Figure 2: Drug discovery timeline

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Figure 3: Possible areas of application of AI in pharmacology. AI: Artificial intelligence

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With the current pandemic, the world experienced the dire need for the development of therapeutics and vaccines in an unprecedented short duration, and many reputed organizations such as Pfizer, Bayer, and Novartis turned their attention to AI/machine learning (ML) for the task.

Furthermore, the COVID-19 pandemic hampered the supply chain management across the sectors including drug and vaccine production. Moreover, like in many other industries, this is a potential area where AI can play a critical role.

Various aspects of drug manufacturing can also benefit from AI, some of which are listed below:

  1. Drug discovery and drug design – One of the most time-consuming processes in the pharmaceutical industry can benefit immensely from AI and ML applications. There are more than 1060 drug-like molecules and experimental studies can only test 105 components per day. AI and computational drug discovery can speed up this process and end up cutting a lot of time and cost involved in the process.[4] AI can facilitate rapid new drug discoveries and create protocols for the synthesis of these new compounds along with the forecast of the preferred chemical structure and an understanding of possible drug–target interactions[5]
  2. AI can revitalize nanomedicine and dose optimization – Diagnostic nanomaterials are used to assemble a patient-specific disease profile, which is then leveraged, through a set of therapeutic nanotechnologies, to improve the treatment outcome.[6],[7] However, high intratumor and interpatient genetic variations make this a very demanding task. Integration of AI approaches using pattern analysis and classification algorithms for improved diagnostic and therapeutic accuracy can help overcome complications and barriers in patient-specific approaches. AI can also boost nanomedicine design process by optimizing material properties according to predicted interactions with the target drug, biological fluids, immune system, vasculature, and cell membranes, all influencing therapeutic efficacy
  3. AI can prove to be instrumental in Quality Control, Quality Assurance, and manufacturing automation protocol, thereby preventing wastages and streamlining pharmaceutical manufacturing processes and supply chains.
  4. Clinical trial design and monitoring – Arguably one of the most time-consuming processes in drug discovery is participant recruitment for clinical trials. AI and ML can help in enrollment, matching/stratification, and protocol designing for clinical trials, and consequently, save a lot of time and money. Moreover, AI can help monitor ongoing trials and ensure adherence to protocols and compliance among the participants. An added advantage with AI and digitization of clinical trials is that these trials are no longer georestricted and as a result, the outcome will be more globally representative rather than to a particular ethnic cohort
  5. Marketing – Like all industries, the pharmaceutical industry too needs to have marketing strategies. AI is already used by many e-commerce sites for effective marketing and this same strategy can benefit the pharmaceutical industry immensely
  6. Prediction of toxicity – The prediction of the toxicity of any drug molecule is essential to avoid adverse effects. Cell-based in vitro assays are often used as the guiding studies, followed by animal studies to pinpoint the toxicity of a compound. This process increases the expense of drug discovery. An automated model, using AI and ML tools, for these processes may very likely prove to be faster, more efficient, and cost-effective
  7. Pharmacovigilance – AI and ML can and will play a pivotal role in speedy and cost-effective processing of ICSR (Individual Case Safety Report) and PMS (Post Marketing Surveillance) data, enabling better drug safety, and personalized therapy.[8],[9] Ever-increasing ADR data further demands the need for AI/ML to process all that data[10]
  8. Geriatric Health care – AI and ML applications can help in precision medicine and hence in personalized health care. The geriatric population often has different dosing needs when compared to the general adult population. With diminished organ functions and slower metabolism, there is a need to personalize dosages and formulations for optimizing therapeutic effects and minimizing toxicities. If AI and ML tools are developed and provided with relevant data, they can help in achieving optimal dosages and formulations for our geriatric population and streamline the health-care delivery for them, which often is a very challenging process.

    AI-powered smart robotic systems are also being developed in the area of assisted living for the elderly. In its nascent state, as it is now, these systems can perform a few mundane tasks such as reminders for doctor's appointments and timely reminders for prescription drugs and can even book a cab to medical facilities. In the near future, these robotic systems may even detect medical emergencies and alert rapid health-care response systems.
  9. Electronic medical records (EMRs) revitalized with AI and ML – Many large tech companies are developing AI and ML tools to convert audio and text medical records into EMRs. A robust system designed in this way can be a boon to developing nations where EMRs are virtually nonexistent. Big tech firm such as Amazon, using its Amazon Web Services platform is also working on such protocols [Figure 4].
Figure 4: Efficient patient care system using Amazon AI protocols (Source: https://aws.amazon.com/blogs/industries/enabling-efficient-patient-care-using-amazon-ai-services/). AI: Artificial intelligence

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  Limitations of Artificial Intelligence in its Current State Top


Despite all the promises, AI comes with its own set of limitations. Like all early-stage techs, AI is still evolving and in its early stages of development and it has a lot to prove. One of the most limiting factors with AI is the requirement of comprehensive training data. This training data requires a lot of human input and humans do make errors and hence AI is also not infallible.

One major apprehension is potential job losses with AI taking over but personally, I believe that it would require an upgradation of skill set as this would create a new set of jobs in the sector.

When used in de novo drug design, AI can predict models/structures that we may currently be unable to manufacture.

AI and ML being completely Information Technology (IT)- based may prove to be challenging for medical field workers and on the flip side IT professionals lack medical knowledge. This means that an interdisciplinary approach to train doctors and IT professionals will have to be developed.

Like all tech at some point, we will require a regulatory system for AI as well and the repercussions of that are yet unclear and uncertain.


  Conclusions Top


It is guaranteed that AI will influence all spheres of our lives and it is evolving and becoming smarter every day. Moreover, AI is sure to revolutionize the pharmaceutical industry too. It is more of a question about “when” rather than “will.” There is still a lot to do before AI can bring about a meaningful change. AI has the potential to be a promising strategy in immensely reducing the cost and time of drug discovery by facilitating the assessment of drug molecules in the initial phases of development. In this era of big data, clinical and pharmaceutical data continue to expand at a feverish pace, and novel AI techniques to deal with big data sets are the need of the hour. The prevailing deep learning modeling studies have shown benefits when compared to the usual ML approaches for this challenge. The next decade may witness seamless integration between human intelligence and AI systems and the pharmaceutical industry cannot remain immune to it. There are currently no drugs on the market that were developed using AI-based methodologies, and there are significant difficulties and challenges in effectively and consistently implementing this technology. Regardless, AI is likely to become an invaluable and indispensable tool in the pharmaceutical industry in the not-too-distant future, and it will remain a cornerstone in the industry for the foreseeable future.

Financial support and sponsorship

Nil

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
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. Pharmaceut Med 2019;33:109-20.  Back to cited text no. 1
    
2.
Iwata, H., Kojima, R., Okuno, Y. (2021). AIM in Pharmacology and Drug Discovery. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-58080-3_145-1.  Back to cited text no. 2
    
3.
Altman RB. Artificial Intelligence (AI) systems for interpreting complex medical datasets. Clin Pharmacol Ther 2017;101:585-6.  Back to cited text no. 3
    
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Kompa B, Hakim JB, Palepu A, Kompa KG, Smith M, Bain PA, et al. Artificial intelligence based on machine learning in pharmacovigilance: A scoping review. Drug Saf 2022;45:477-91.  Back to cited text no. 4
    
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Wen M, Zhang Z, Niu S, Sha H, Yang R, Yun Y, et al. Deep-Learning-Based drug-target interaction prediction. J Proteome Res 2017;16:1401-9.  Back to cited text no. 5
    
6.
Minko T, Rodriguez-Rodriguez L, Pozharov V. Nanotechnology approaches for personalized treatment of multidrug resistant cancers. Adv Drug Deliv Rev 2013;65:1880-95.  Back to cited text no. 6
    
7.
Sun M, Zhao S, Gilvary C, Elemento O, Zhou J, Wang F. Graph convolutional networks for computational drug development and discovery. Brief Bioinform 2020;21:919-35.  Back to cited text no. 7
    
8.
Liang G, Fan W, Luo H, Zhu X. The emerging roles of artificial intelligence in cancer drug development and precision therapy. Biomed Pharmacother 2020;128:110255.  Back to cited text no. 8
    
9.
Murali K, Kaur S, Prakash A, Medhi B. Artificial intelligence in pharmacovigilance: Practical utility. Indian J Pharmacol 2019;51:373-6.  Back to cited text no. 9
[PUBMED]  [Full text]  
10.
Das S, Dey A, Pal A, Roy N. Applications of artificial intelligence in machine learning: Review and prospect. Int J Comput Appl 2015;115:31-41.  Back to cited text no. 10
    


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