INTRODUCTION:^1,2,3

AI is a stream of science related to intelligent machine learning, mainly intelligent computer programs, which provides results in a similar way to the human attention process¹.This process generally comprises obtaining data, developing efficient systems for the uses of obtained data, illustrating definite or approximate conclusions,self-corrections, and adjustments² In general, AI is used for analyzing machine learning to imitate the cognitive tasks of individuals ^2,3 AI technology is exercised to perform more accurate analyses as well as to attain useful interpretation Clinical Trials.³

1] Drug Discovery and Design:^4,5,6

The process of Drug Discovery affects the whole pharmaceutical sector including all the phases such as a preliminary phase of research from target Discovery and validation to the discerning of molecules. A variety of streams can be used to initiate the identification of small therapeutic molecules ⁴.New research can lead to awareness of different diseases with different routes of drug administration and can be developed to take part in pharmaceutical companies to run large-scale trials and other waste programs, in order to identify the targeted molecular compounds. This process is mostly performed at the beginning of lead Discovery, with the prospect of taking identified compounds in the right way through preclinical and clinical trials.^5,6

Figure 1. Key steps of drug discovery

Target Identification and Validation^7,8,9,10

There are two paradigms for discovering new (first-in-class) drugs⁷phenotypic drug discovery (PDD) and target-based drug discovery (TDD). Early biological research techniques relied on microscopy, imaging, and cellular techniques to observe the phenotypic changes in living systems. PDD is used to screen a library of compounds or antibodies by constructing an animal model or experiment that is highly relevant to the disease. Next, the responses of cells or experimental animals to these compounds are observed; with the aim of identifying molecules with a certain level of efficacy for further structural modification and optimization.⁸ With the development of molecular biology and various sequencing techniques, research on biological macromolecules has reached a new height. Drug discovery research has entered the TDD era⁹ and TDD has gradually replaced PDD as the mainstream drug discovery paradigm.TDD is centered on a ‘‘one gene, one drug, and one disease” concept¹⁰This approach relies on a highly disease-relevant target, which could be an enzyme, protein, or other gene product, along with an elaborate and meticulous small-molecule design for this target, which is used to modulate the target to act as a therapeutic agent for the disease. Although the drug discovery paradigm of PDD has been re-emerging in recent years⁸, the screened drugs often require further target validation and mechanistic studies. Therefore, target discovery is often the first, critical step in the drug development phase⁹ The target discovery process involves multifaceted research, including the study of disease-related genes, signaling pathways, protein interactions, and small molecule protein interactions. Of particular interest is the fact that target discovery based on experimental means is difficult to carry out quickly and widely, due to limitations in throughput, accuracy, and cost, whereas AI-based discovery can efficiently and effectively identify biomolecules with the potential to become drug targets.

The first step involves target identification and validation as it starts the whole drug discovery process.The basic targets for therapeutic use are cellular on modular structures which materialize naturally and take part in prime roles in pathogenicity⁶ The target molecule should be conveniently safe and efficacious and should meet the clinical demands of the patient to get the desired drug molecule after identification of the drug target system approach should be performed in the mode of action of the API to be qualified for efficacy.

Drug Screening¹¹

The use of AI in drug screening includes target identification; molecular simulations; drug property predictions; de novo drug creation, synthesis of route generation, and candidate drug prioritization.¹¹

Lead Optimization^12,13,14,15

To develop a preclinical drug candidate, it is necessary to improve on the deficiencies of lead compounds and maintain their desired properties. Using this step, you can determine whether your drug metabolizes in the right area of your body, or whether you have any current side effects of concern. An integrated approach is recommended for the same. Bringing together experts in computational chemistry, medical chemistry, drug metabolism, and other fields will allow them to provide unique insight into this late stage of the process.^{12,
13, 14}

Before progression to preclinical and clinical trials, late-stage optimization, in which further pharmacological safety of a lead compound is assessed, is a vital step. A drug’s efficacy, pharmacokinetics, and safety will be more likely to be compromised later in development if this phase is ignored. As part of safety optimization, the aim is to identify and progress leads with the best overall safety profile, remove the most toxic leads, and establish a well-defined hazard and translational risk profile necessary for further in vitro testing. By thoroughly calculating the risks at this point, more opportunities can be taken when investments are made into leads. Further, if the drug is approved for development it is passed to perform preclinical and clinical trials.¹⁵

2] Clinical Trials¹⁵

For recognizing patient conditions, finding gene targets, forecasting the outcome of a created drug, and on and off-targets, a generation of AI tools for clinical trials would prove excellent. In Phase II clinical studies, one AI mobile application boosted medicine adherence by 25% compared to conventional direct observation therapy.¹¹

Patient Recruitment^16,17,18

An appropriate AI tool to aid in clinical trials should identify the disease in patients, identify genetic targets and evaluate the impact of the designed molecule as well as on and off-target effects ¹⁶. The development of AI methods for detecting and predicting disease-related biomarkers in humans allows the recruitment of a specific population of patients in Phase II and III clinical trials. AI predictive modelling is successful in clinical trials in selected patient populations ¹⁷. Selection of participants for the trial is the most important step where patients/participants’ health record provides vital information for matching the inclusion or exclusion criteria. Collecting the patients ‘data/history or fresh testing would be time-consuming and costly. AI provides an opportunity to combine patient data with the electronic medical record (EMR) including omics data and other patient data, scattered among different locations, owners, and formats. Suchanalysis using computer vision algorithms such as optical character recognition (OCR) and Natural language processing (NLP) can provide an efficient process in patient identification and characterization.¹⁸

Protocol Design^18,19,20,21

As per the FDA, AI models are useful in improving the quality of trial design, patient selection by reducing population heterogeneity, prognostic enrichment, and predictive enrichment.¹⁸Bayesian nonparametric models (BNMs) have emerged as a powerful tool in clinical trial design with many other applications. This model is flexible and uses a nonparametric approach. This model allows us to use infinite-dimensional parameter sets with a finite subset of limited parameters. This approach minimizes the clustering and trial designing duration. Some of the commonly used BNMs are Dirichlet process mixture models and Markov Chain Monte Carlo (MCMC) techniques. There are many applications of such BNMs in clinical trial design, for example, the dose selection in clinical trials involving cancer patients, immune-oncology and cell therapy trials. Dose selection is complicated due to the heterogeneity of the patients, which may lead to inaccurate dose selection and selection of future target populations. BNMs are an efficient and effective tool for dose selection in such patients because it considers all the variable and heterogeneity of the study subjects.^[19] Bayesian nonparametric design is used for adaptive dose selection in multiple populations. This facilitates the borrowing of information across multiple populations while considering the heterogeneity of the populations. Such models help in accurate optimal dose selection, which minimizes the inaccuracy.²⁰ Other designs such as modified toxicity probability interval (mTPI) designs use the Dirichlet process.

This design learns from the emerging data and selects the dose by prior approximation and automatically groups patients into similar clusters.²¹

3] Drug Repurposing^22,23,24

It is also called drug repositioning, and defined as the process to estimate or find a new approach of the approved drugs.²² The process of drug repositioning is more attractive and practical with the help of AI. The idea of using existing treatments for a new disease is advantageous because the new appropriate drug bypasses the Phase I trial which includes the toxicity studies and it goes directly to Phase II clinical trials with a different indication.²³ Drug repositioning is possible to begin because most drugs may have multiple targets and targets may have multiple effects, leading to higher variability in the drug-disease relationship with drug disorders. AI applies to drug repositioning as it provides short association information to the target population.²⁴

4] Personalized Medicine^25,26,27,28

AI has the potential to derive a meaningful relationship within the raw datasheets that can be further used in the diagnosis, treatment, and mitigation of the disease. A variety of newer techniques which are used for computational understanding in this emerging field have the potential to be applied in almost every field of medical science. The complex clinical problems need to be solved with the challenge of acquiring, analyzing, and applying vast knowledge (Fig 2). The development of medical AI has helped clinicians to solve complex clinical problems. The systems such as ANNs, evolutionary computational, fuzzy expert systems and hybrid intelligent systems can assist the healthcare workers to manipulate the data.²⁵ The ANN is a system that is based upon the principle of the biological nervous system.²⁶There is a network of interconnected computer processors called neurons that can perform parallel computations for data processing. The first artificial neuron was developed using a binary threshold function.²⁷ The multilayer feed-forward perceptron was the most popular model having different layers, such as input layer, middle layer, and output layer. Each neuron is connected through links having numerical weight.²⁸

Figure 2. AI in acquiring and analyzing data of a patient in personalizing the treatment

5] Drug Manufacturing^29,30,31

Manufacturing complexity has increased from prior years as a result of recent improvements in pharmaceutical technology and distinctive developments. For the complex procedure, a proven product's excellence and effectiveness are also necessary. Modern pharmaceutical production systems are striving to merge human expertise into machines to enhance manufacturing processes. The employment of AI in the production process has the potential to expand the pharmaceutical industry. Numerous pharmaceutical activities will be mechanized with the aid of AI tools. In addition to addressing problems that may occur during the manufacturing process, this can aid in numerical stimulation.²⁹

AI is being used to develop new pharmaceutical products, including the following:

§ Disease Identification/Diagnosis

§ Personalized treatment with digital therapeutics and behavioral modification

§ Drug Development and Production

§ Prognostic forecasting

§ Medical Tests

Figure 3. AI in Drug Development

The introduction of a novel medicine to the commercial market is a complicated and lengthy procedure that normally takes several years and involves significant financial expenses due to a high attrition rate. As a result, there is a pressing need to optimize this process employing cutting edge technology like artificial intelligence (AI) (Figure 3). The FDA has recently advocated for the use of real-world data (RWD) in medication development.³⁰

The other uses of AI in drug development involve the forecasting of feasible synthetic routes for drug like molecules, pharmacological properties, protein characteristics in addition as efficacy, drug combination and drug target association and drug repurposing. Also, the spotting of recent pathways and targets using omics analysis becomes practicable via the creation of novel biomarkers and therapeutic targets, personalized medicine supported omics markers and discovering the connections between diseases and medicines.³¹

6] Applications of AI in Pharmaceuticals and Drug delivery^32,33

The power of long-term learning is often removed after training when an AI system is employed to regulate processes like manufacturing or clinical trials. The pharmaceutical industry has improved since the relatively recent adoption of Quality by design (QbD) methodologies, nevertheless, and the latest industry 4.0 initiatives seems to portray a sector in rapid development.³² Therefore, there is a strong likelihood that if an early AI application is developed, it will be put into use. In contrast to other scientific fields, pharmaceutical sciences can cause delays in data codification and standardization. Data accumulation and standardization are essential for effectively training AI in the former.³³

AI used ³⁴

The following are some examples of how AI is used in data processing:

1. Data searching and search engine optimization to produce the most pertinent results.

2. If–then logic chains that can be used to carry out a series of instructions dependent on parameters.

3. Pattern detection to find noteworthy patterns in vast data sets for original insights

4. using probabilistic

· Maintaining of medical records

Keeping up with patient medical records is a difficult endeavour. Data gathering, storing, normalisation, and tracking are made simple by putting the AI system into use. In a brief amount of time, the Google Deep Mind Health Project helps to retrieve medical records. Therefore, this initiative is helpful for providing healthcare more quickly and effectively. This project contributes to improving eye treatment at the Moor fields Eye Hospital NHS.

· Treatment plan designing

AI technology makes it feasible to create treatment programmes that are both effective and efficient. When a patient's critical condition emerges and choosing an appropriate treatment strategy is challenging, the AI system isrequired to maintain control over the situation. The treatment plan recommended by this technology takes into account all of the prior data and reports, clinical expertise, etc. The software as a service IBM Watson for Oncology is a cognitive computing decision support system that gives cancer clinician’s information about treatment options after analysing patient data against thousands of past cases and insights gained from working thousands of hours with Memorial Sloan Kettering Cancer Centre physicians. Memorial Sloan's selection of literature supports these therapy approaches.

· Health support and medication assistance

AI technology has been shown to be effective in recent years for both pharmaceutical assistance and health support services. Molly, the virtual nurse created by Start-Up, is greeted with a friendly face and voice. Its goal is to support patients with their chronic ailments during doctor appointments and assist them in directing their own treatment. A programme called AI Cure, which works with a smartphone's webcam, keeps an eye on patients and helps them manage their ailments. Patients who take part in clinical trials and those with severe drug conditions can both benefit from this app.

· Accuracy of medicine^35,36

AI has a positive effect on genetic development and genomics. An AI system is helpful for identifying patterns in the data from Deep Genomics. To determine the mutations and connections to diseases, use genomic data and medical records. This technique provides physicians with information on what happens inside a cell when genetic variation modifies DNA. Craig Venter, the creator of the human genome project, created an algorithm that uses a patient's DNA to provide physical traits. "Longevity in Humans" When vascular illnesses and cancer are in their early stages, AI technology can be used to pinpoint their precise location.

· Drug creation

Pharmaceuticals require billions of rupees and more than ten years to manufacture or create. The AI programme "Atomwise", which makes use of supercomputers, is helpful in determining the treatments from the molecular structure database. It launched a virtual search programme for an Ebola treatment that is both safe and effective using currently available medications. Two medications that spread the Ebola virus were found by the technology. In contrast to months or years when analysis was done by hand, this study was finished in a single day. Big data was created by a Boston-based Biopharma company to help with patient care. It stores information to determine the causes of some patients' illness survival. Utilising AI technology and biological data from patients, they were able to determine the distinction between environments conducive to disease and those that were not.

7] Future scope³⁷

The quality of the results produced by AI algorithms depends on the availability of datasets for training. AI algorithms learn from data. Many complex challenges in drug design include information selection, data modelling, categorization, prediction, and optimisation. These challenges all promote the creation and application of specific artificial intelligence (AI) systems. Artificial intelligence (AI) is being used to generate predictive fingerprints of illness states, progression, and results of therapeutic interventions, as well as to identify connections between patterns of genetic variations and expression profiles and clinical and other characteristics. Before spending money on an actual clinical trial, CTS research use computational simulation techniques on preselected populations to assess alternative trial designs. The majority of respondents (59%) stated that their business intended to increase the number of employees using AI.³⁷

8] Limitations of AI at the present moment³⁸

Even though artificial intelligence (AI) has several applications in the pharmaceutical sector, there are significant limitations. AI needs to be developed in order to reach the current standards of standard outcomes. The requirement for comprehensive training data for AI is one of the things that point to its limitations because it requires human labour and so has the potential to be inaccurate. AI integration is therefore necessary. AI's ability to foresee models or structures through the construction of de novo medications which is hard to produce is another disadvantage. Healthcare personnel must be prepared for the use of AI through a multidisciplinary approach because AI and ML are IT-based technologies, which could be challenging for the sector. ³⁸

9] Advantages of AI technology^39,40,41

The potential advantages of AI technology are as follows:

1. Error minimization: Artificial Intelligence helps to reduce errors and improve accuracy more precisely. Robots with intelligence are composed of robust metal bodies. They are dispatched to explore space because they are resilient and able to withstand the harsh atmosphere there.

2. Difficult exploration: AI proves to be beneficial in the mining industry. The field of fuel exploration also makes use of it. AI programmes are able to look into the ocean by eliminating the mistakes made by people.

3. Daily application: AI is very useful for our daily acts and deeds. For examples, GPS system is broadly used in long drives. Installation of AI in Androids helps to predict what an individual is going to type. It also helps in correction of spelling mistakes.

4. Digital assistants: Now-a-days, the advanced organizations are using AI systems like ‘avatar’ (models of digital assistants) for the reduction of human needs. The ‘avatar’ can follow the right logical decisions as these are totally emotionless. Human emotions and moods disturb the efficiency of judgement and this problem can be overcome by the uses of machine intelligence.

5. Repeated tasks: Humans can typically handle one repetitive task at a time. Dissimilar to humans, machines possess the ability to multitask occupations and are able to analyse information faster than humans. It is possible to modify different machine characteristics, such as speed and time, in accordance with their needs.

6. Medical uses: Generally speaking, doctors are able to evaluate patients' conditions and examine any negative effects or additional health concerns connected to the medicine with an AI program's assistance. AI applications such as different artificial surgical simulators (e.g., heart, brain, and gastrointestinal simulations) can be used by trainee surgeons to acquire expertise.

7. No breaks: Unlike people, who can work for eight hours a day without a break, computers are programmed such that these are able to work continuously for extended periods of time without experiencing any kind of disorientation or boredom

8. Increase the rate of technical development: AI is a major component of the majority of cutting-edge technological advancements made globally. It has the ability to generate various computer modelling applications and strives to create novel compounds. Drug delivery formulations are being developed with the aid of artificial intelligence technologies.

9. Not risk: Working in hazardous environments, such as fire stations, increases the likelihood that the persons involved may suffer damage. Regarding the machine learning programmes, so that damaged parts can be fixed in the event of an accident.

10. Acts as aids: Artificial intelligence technology has taken on a new role by providing round-the-clock assistance to both elderly and young. It can be used for instruction and learning sources for everything.

10] Disadvantages of AI technology^39,40,41

The important disadvantages of AI technology are as follows:

1) Expensive: AI adoption results in significant financial outlays. Intense machine design, upkeep, and repair are incredibly economical. The research and development division needs a lot of time to design a single AI machine. Software for AI machines needs to be updated on a regular basis. Reinstallations and machine recovery take a long time and cost a lot of money.

2) No replicating human: Artificial intelligence (AI)-enabled robots are said to possess human-like intelligence and emotionlessness, which confers some benefits to complete the assigned work more precisely and without bias. In the event of unknown problems, robots are unable to make decisions and may produce inaccurate reports.

3) No improvement with experience: Human resource can be improved with experiences. In contrast, machines with AI technology cannot be enhanced with experience. They are unable to identify which individual is hard working and which one is nonworking.

4) Absence of inventiveness: Artificial intelligence (AI) machines lack both emotional intelligence and sensitivity. People are able to hear, see, and think and feel. In addition to their thinking, they can employ their creativity. Machines are not capable of achieving these features.

5) Unemployment: The extensive use of AI technology across all industries could result in a significant increase in joblessness. Due to the unfavourable rate of unemployment, human labourers may become less creative and prone to bad work habits.

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Received on 17.04.2024 Modified on 01.05.2024

Research J. Science and Tech. 2024; 16(2):129-136.

DOI: 10.52711/2349-2988.2024.00020