INTRODUCTION:

Numerous sectors are endeavoring to advance their development to meet the needs and expectations of their clientele, employing diverse methodologies. Among these, the pharmaceutical industry stands out as a critical domain, playing an indispensable role in preserving lives. It thrives on continual innovation and the integration of novel technologies to tackle global healthcare challenges and respond to medical crises, such as recent pandemics¹. Innovation within the pharmaceutical realm hinges on extensive research and development spanning various aspects, including manufacturing technologies, packaging considerations, and customer-centric marketing strategies². From small drug molecules to biologics, novel pharmaceutical innovations prioritize stability and potency to address unmet medical needs. Assessing the significant toxicity levels associated with new drugs remains a paramount concern, necessitating ongoing research and exploration. The primary objective is to furnish drug molecules that offer optimal benefits and suitability for adoption within the healthcare sector. Despite progress, the pharmaceutical industry grapples with numerous hurdles, underscoring the need for further technological advancements to meet global medical and healthcare demands. Sustaining a skilled workforce in healthcare necessitates continuous training to enhance their proficiency in routine tasks. Identifying skill gaps within the pharmaceutical sector is imperative, with remedial measures essential for addressing these gaps effectively. Supply chain disruptions have emerged as a formidable challenge, with pharmaceutical industries striving for innovative solutions to bolster business resilience. The outbreak of the COVID-19 pandemic has exacerbated disruptions, affecting various global operations, including clinical trials. Supply chain disruptions stemming from pandemics, natural disasters, pricing fluctuations, cyberattacks, and logistical delays necessitate adaptive strategies. Transportation challenges and decision-induced delays exacerbate these disruptions, underscoring the need for agile approaches in cross-border trade cooperation and resource availability. Adaptations in manufacturing footprints are essential to accommodate patient needs and regulatory compliance. In the realm of computer science, there exists a discipline known as artificial intelligence (AI), which focuses on employing symbolic programming to tackle various problems. Over time, AI has evolved into a problem-Solving discipline with wide-ranging applications in industries such as pharmaceuticals, medicine, and Engineering. Its primary objective is to identify practical information processing challenges and offer abstract Solutions for addressing them. Within the domain of artificial intelligence, there exists a mathematical theorem known as a “method” which pertains to such solutions. Algorithms are crafted and utilized to analyse and comprehend data in this field, encompassing various branches such as statistical and machine learning, Analytical thinking, clustering, and similarity-based techniques. AI is a swiftly advancing technology with Numerous applications in both commercial endeavors and everyday activities. Notably, the pharmaceutical Industry has recently explored novel and innovative ways to leverage this powerful technology to tackle some of its most pressing concerns. In pharmaceuticals, artificial intelligence encompasses the use of machine Learning to perform tasks that traditionally necessitate human intelligence. The integration of artificial Intelligence in the pharmaceutical and biotechnology sectors has transformed how researchers develop new Medications, treat illnesses, and more over the past five years.³

Limitations of the Current Methods in Drug Discovery:

Presently, the field of medicinal chemistry predominantly employs a trial-and-error methodology alongside extensive testing procedures⁴. These methods involve screening numerous compounds to identify ones with desired characteristics. However, they are often slow, expensive, and prone to inaccuracies⁵. Moreover, they face constraints such as limited access to suitable test compounds and challenges in predicting their behaviour within the body⁶.

Alternatively, various AI-driven algorithms, including supervised and unsupervised learning, reinforcement learning, evolutionary algorithms, and rule-based systems, hold promise in addressing these issues. These algorithms leverage extensive data analysis to predict the efficacy and toxicity of new drug candidates more accurately and efficiently than traditional methods^7-9. Additionally, AI can assist in identifying novel drug targets, such as disease-related proteins or genetic pathways, thereby expanding the horizons of drug discovery and potentially leading to the development of more potent medications¹⁰.

While traditional pharmaceutical research methods have achieved some success, their reliance on trial and error and their limited predictive capabilities hinder progress. In contrast, AI-driven approaches offer the potential to enhance the efficiency and precision of drug discovery processes, ultimately resulting in the creation of more efficacious medications^11,12.

AI’s Role in Predicting Drug Effectiveness and Safety: AI plays a crucial role in medicinal chemistry by predicting the effectiveness and safety of potential drug compounds. Traditional drug discovery methods often involve laborious experimentation to assess a compound’s impact on the human body, which is slow, costly, and uncertain. AI techniques, however, can address these challenges by analysing vast amounts of data to uncover patterns and trends not easily discernible to human researchers. This accelerates the identification of new bioactive compounds with minimal side effects compared to conventional protocols¹³.

For example, deep learning algorithms have shown promise in accurately predicting the activity of novel compounds based on training data of known drug compounds. AI models trained on extensive databases of toxic and non-toxic compounds have also made significant strides in preventing drug toxicity¹⁴.

Another vital application of AI in drug discovery is identifying drug-drug interactions, which can lead to altered effects or adverse reactions when multiple drugs are combined. AI-based approaches analyse large datasets of known drug interactions to recognize patterns and trends, facilitating the prediction of interactions between novel drug pairs.

Furthermore, AI contributes to personalized medicine by identifying potential drug-drug interactions tailored to individual patients based on their genetic profile and medication response. This allows for the development of personalized treatment plans to minimize adverse reactions.

The literature showcases how AI enhances pharmaceutical research by improving the prediction of drug efficacy and toxicity, ultimately leading to the development of safer and more effective medications while expediting the drug discovery process¹⁵.

AI is employed in pharmaceutical research for pinpointing targets, refining leads, and designing clinical trials. It sifts through extensive data sets to forecast drug characteristics, pinpoint possible drug targets, and expedite the entire drug development process, enhancing the efficiency and efficacy of pharmaceutical discovery^16,17.

Application of AI in Drug Discovery:

Target Identification and Validation:

AI-powered drug discovery expedites the process of pinpointing and validating potential molecular targets by analysing various datasets, including drug databases and public repositories. Using methodologies such as deep auto encoders, relief algorithms, and binary classification, AI-driven drug discovery effectively prioritizes these targets.

Moreover, AI platforms harness graph-convolutional networks and computer vision models trained on cryo-EM microscope data to comprehend protein structures.

Compound Screening and Lead Optimization:

In compound screening, AI-driven virtual screening enables the practical identification of potential lead molecules from extensive compound databases. The automated AI Retrosynthesis Pathway Prediction significantly enhances the planning process for chemical syntheses.

Furthermore, AI-based drug discovery models play a crucial role in categorizing cell targets and facilitating intelligent image-activated cell sorting, leading to more efficient cell separation.

Preclinical Studies:

AI is crucial in comprehending the molecular mechanisms of action and predicting dose-response relationships in pharmacokinetic/ pharmacodynamic modeling. It streamlines toxicology assessments through the Deeptox Algorithm, providing precise predictions of compound toxicity.

Additionally, deep learning algorithms utilize transcriptomic data to make accurate predictions regarding pharmacological properties.

Clinical Trials:

AI tools play a vital role in optimizing various aspects of clinical trials. They aid in identifying patient diseases, pinpointing specific gene targets, and forecasting molecular effects. Furthermore, AI-driven applications enhance medication adherence and streamline risk-based monitoring, thereby increasing the efficiency and success rates of clinical trials¹⁸.

AI Techniques for Advancing Drug Delivery Systems:

Typically, the development of drug delivery systems faces challenges such as predicting how formulation factors relate to responses, impacting therapeutic outcomes and unexpected events. In designing intelligent drug release systems, factors like on-demand dose adjustment, drug release rates, targeted release, and stability are crucial. For drug release self-monitoring systems, appropriate algorithms play a key role in controlling the quantity and duration of drug release. Hence, AI approaches are valuable for predicting the efficacy of drug dosing and the delivery potential of drug delivery forms^19,20.

Solid dispersions:

Researchers have utilized ANN modeling in combination with experimental design to create solid dispersions of carbamazepine using poloxamer 188 and Soluplus®. The goal was to enhance the solubility and dissolution rate of carbamazepine. These solid dispersions were formed using the solvent casting technique. In a study, ANN modeling with feed-forward back propagation and logistic sigmoid activation function was employed to analyze the relationship between various variables and dissolution properties to optimize the drug’s dissolution rate. To prepare the solid dispersions, poly(vinyl pyrrolidone)/polyethylene glycol mixtures were used as carriers. The ANNs-assisted modeling provided accurate predictions for the solid dispersion formulations, ensuring desired dissolution properties and long-term physical stability^21,22.

Emulsions and microemulsions:

ANN technology has been employed in the development of stable emulsions (oil/water)²³. The optimization of fatty alcohol concentration for oil/water emulsions was studied, analyzing independent variables such as lauryl alcohol concentration and time, while dependent variables included droplet size, zeta potential, viscosity, and conductance. Validation testing showed excellent correlation between ANN-predicted values and experimental data²⁴. ANN has also been utilized in formulating microemulsions, accurately predicting their characteristics based on formulation. Genetic algorithms and evolutionary ANNs have been combined to forecast interior structural features and microemulsion properties with high precision²⁵. In another study, ANN modeling was used to predict the formulation of stable microemulsions containing antitubercular drugs like rifampicin and isoniazid for oral administration²⁶. Data from pseudo-ternary phase diagrams representing oil components and surfactant mixtures were used for testing and validating the ANN modeling.

Tablets:

In the development of matrix tablets, both static and dynamic ANNs have been utilized to model the dissolution profiles of various matrix tablets²⁷. Monte Carlo simulations and genetic algorithms were employed for modeling based on ANN algorithms. The researcher utilized Elman dynamic neural networks and decision trees, which accurately predicted the dissolution properties of both hydrophilic and lipid-based matrix tablets with controlled drug release patterns²⁷. Compared to commonly used multilayer perceptron and static networks, Elman neural networks-based modeling efficiently captured the drug release patterns of various formulations of hydrophilic and lipid-based matrix tablets. In another study, matrix tablets for sustained release of an antidiabetic drug, metformin HCl, were developed using multilayer perceptron with feed forward back propagation technique²⁸. The in vitro release pattern of metformin HCl from the matrix tablets was optimized to develop the ideal formulations. Independent variables and dependent variables were analyzed for network training, and the leave-one-out technique was employed for model validation through several trials. Additionally, ANNs were used for the formulation optimization of nimodipine matrix tablets for controlled release applications²⁹. A combination of ANN-based modeling and statistical optimization was employed for the formulation design of glipizide releasing osmotic pump tablets³⁰. Besides dissolution testing, various formulation and process variables were optimized and analyzed using ANNs. A blend of response surface methodology and ANN-based modeling was applied for the formulation optimization of osmotic tablets containing isradipine³¹. The disparity between predicted and observed dissolution results for the optimized isradipine osmotic tablets fell within experimentally induced error limits. Moreover, there was no significant difference between predicted and observed dissolution results, demonstrating the suitability of ANN-based modeling for achieving the desired dissolution pattern in the development of controlled-release osmotic tablets containing isradipine.

AI Tool Application in Dosage Form Designs:

The human body is compartmentalized to understand how drug delivery affects different areas. These compartments are simplified based on biological membranes, which are crucial physicochemical barriers for biological compartments. They dictate how drugs are delivered within the body. Monitoring the efficiency of a drug delivery system relies heavily on its permeation rate according to the administration route. For instance, orally administered drugs must permeate through the intestinal or gastric epithelium after entering the gastric environment to distribute further into the bloodstream. Distribution then transports the drug to the target site, which can be tissues or specific cellular components^32-35. Intracellular molecules can also serve as targets for drug entry. Most drug permeation occurs through biological barriers, either passively or actively. Passive diffusion depends on the drug’s molecular properties. In silico models are used to predict drug distribution computationally, but actual results may vary. The drug’s interaction with biological components and its availability in biological environments significantly influence its fate in the body, guided by its molecular features. For many biologically active compounds and small molecules, passive permeation is inadequate, necessitating specific drug delivery systems. Active permeation relies on membrane transport and complex biological interactions, necessitating exploration through computation and systematic modeling approaches. These newer computational models study the pharmacokinetic parameters of drug delivery systems. A major challenge in pharmaceutical research and development is the predictability of preclinical models, which relies on selected parameters. This applies to complex in silico models as well, all linked to drug-membrane interactions and effectively analyzed through modeled environments, benefitting from AI technology^36-38. AI offers sophisticated analysis of multilayer data, contributing to a deeper understanding of research units. A systematically applied model, combined with parameter evaluation, employs various factors such as simulation, scoring, and refinement at each research step to yield optimal outcomes. AI facilitates automation for better data prediction and refinement, leading to consistent improvement. Comprehensive AI training in the biological environment requires a thorough understanding of drug-biological interactions, as indicated by systems biology databases. Novel AI technologies, like artificial neural networks, enable pharmacokinetic studies, while AI databases provide insights into chemical, genomic, and phenotypical aspects for better understanding drug interactions and studying complex molecule roles. Various methods are employed to assess the impact of drug delivery systems on drug pharmacokinetics, aiding in understanding disposition and toxicity. Emerging approaches to drug delivery system design focus on quality attributes and critical attributes, studying their impacts through experimental trials before actual implementation.

AI in Medical Devices:

The medical equipment refers to various tools, implements, instruments, implants, or machines designed for specific medical purposes. These can operate independently or in conjunction with software or related systems in controlled environments to address patients’ medical needs. Artificial intelligence (AI) has significantly progressed in the medical equipment domain, transforming healthcare practices in diverse ways. The current global situation has emphasized the importance of personalized medicine and remote health monitoring, leading to increased adoption of AI and machine learning technologies in healthcare settings.

Several applications demonstrate how AI is integrated into medical equipment:

· Diagnostic Support: AI algorithms analyze medical imaging data, such as X-rays, CT scans, and MRIs, aiding healthcare professionals in disease detection and diagnosis. For instance, AI-powered algorithms assist in identifying cancerous lesions in medical images or detecting abnormalities in electrocardiograms (ECGs)⁴¹.

· Remote Monitoring: AI-enabled medical equipment remotely monitors patients’ health conditions, facilitating continuous tracking of vital signs and relevant parameters. This is particularly beneficial for individuals with chronic illnesses, enabling personalized care from home. AI algorithms analyze collected data and offer alerts or insights to healthcare providers⁴².

· Wearable Devices: AI is incorporated into wearable devices like smart watches, fitness trackers, and biosensors, monitoring various health indicators such as heart rate, sleep patterns, physical activity, and blood glucose levels. AI algorithms interpret data and provide users with actionable insights to enhance their health and well-being⁴³.

· Prosthetics and Rehabilitation: AI enhances advanced prosthetic devices, enabling more natural movement and functionality. Machine learning algorithms learn from user actions, adjusting prosthetics to align with the user’s intentions. Additionally, AI aids in rehabilitation by analyzing motion and providing feedback to patients, facilitating movement improvement and progress tracking⁴⁴.

· Surgical Support: AI finds application in surgical equipment, assisting surgeons during procedures. Robotic surgical systems utilize AI algorithms to aid surgeons in performing precise and minimally invasive surgeries. AI analyzes preoperative and intraoperative data, offering real-time guidance and enhancing surgical outcomes⁴⁵.

· Medication Management: AI-powered devices assist patients in effectively managing medications. Smart pill dispensers remind patients to take medications on schedule, dispense accurate dosages, and monitor adherence. AI algorithms analyze patient data, including medical history and medication usage, to offer personalized medication management recommendations^46,47.

Artificial Intelligence for Pharmacokinetics and Pharmacodynamics:

The development of medications involves various stages, such as drug discovery, preclinical assessments, clinical trials, and regulatory clearance. Understanding pharmacokinetics and pharmacodynamics is pivotal in determining the appropriate dosage, administration method, and safety profile of a medication within the body⁴⁸. Traditional experimental approaches for studying pharmacokinetics and pharmacodynamics can be time-consuming, costly, and sometimes fail to provide precise forecasts regarding a drug’s effectiveness and safety⁴⁹.

Traditionally, investigations into pharmacokinetics and pharmacodynamics have relied on experimental techniques like animal studies and human clinical trials. However, these methods pose significant challenges, including ethical considerations, limitations in sample size, and variations among individuals. Additionally, they may not consistently offer accurate projections of how drugs will behave within human systems. To address these constraints, computational models and AI techniques have been developed to forecast drug pharmacokinetics and pharmacodynamics more swiftly, economically, and accurately^50,51.

AI demonstrates immense promise in the realms of pharmacokinetics, pharmacodynamics, and drug development. With the advancements in computing power and machine learning algorithms, AI has emerged as a valuable asset for predicting and enhancing drug pharmacokinetics and pharmacodynamics. Despite challenges related to handling extensive datasets and ensuring data reliability, AI holds the potential to revolutionize pharmacokinetics-pharmacodynamics studies and their impact on therapeutic interventions.⁵²

Insights from human experts on ChatGPT and AI-driven tools for scientific writing:

As highlighted in preceding sections, AI holds significant potential across various phases of drug discovery, spanning from initial drug design to eventual market release. Nevertheless, the impact of AI transcends these realms and can profoundly enhance the processing and analysis of scientific literature. Integrating AI into literature reviews and article composition within the field of drug design presents tremendous opportunities. AI algorithms can expedite the review process, offer comprehensive insights from diverse data sources, and aid in identifying novel research avenues. Additionally, AI-powered writing tools can elevate the quality and efficiency of scientific writing, empowering researchers to effectively communicate their discoveries. By embracing AI in these capacities, we not only save time and resources but also elevate the overall caliber of drug design research, propelling us closer to the development of groundbreaking and life-changing therapies.

ChatGPT, a chatbot based on the GPT-3.5 language model (as of the manuscript’s preparation), was not initially designed as a scientific paper writing assistant. However, its capability to engage in coherent human-like conversations and offer insights across diverse topics, including generating and correcting computational code, has intrigued the scientific community. Hence, we decided to assess its potential contribution to drafting a brief review on AI algorithms’ role in drug discovery. As an AI assistant for scientific paper writing, ChatGPT presents several advantages, including its ability to swiftly generate and refine text and assist users in organizing information or connecting ideas. Nonetheless, it falls short as a standalone content generation tool. Our review of the AI-generated text, following our instructions, necessitated significant edits and corrections, notably replacing nearly all references due to inaccuracies provided by the software. This presents a major challenge with ChatGPT, notably differing from typical web browsers focused on delivering reliable references. Another notable issue with the utilized AI-based tool is its 2021 training, rendering it incompatible with updated information. While many of these challenges could potentially be resolved swiftly, they introduce new and pressing challenges concerning apparent new content control.

Following this experiment, it is evident that ChatGPT is not a viable tool for generating reliable scientific texts without substantial human intervention. The program lacks the requisite knowledge and expertise to accurately convey intricate scientific concepts and information adequately. Furthermore, ChatGPT’s language and style may not align with academic writing standards. Human input and review are indispensable for producing high-quality scientific texts. One primary hindrance to ChatGPT’s readiness for scientific article production is its inability to assess the veracity and reliability of processed information. Consequently, scientific texts generated by ChatGPT are prone to errors or misinformation. Additionally, reviewers may encounter challenges distinguishing between human-authored and AI-generated articles, necessitating rigorous review processes to safeguard against false or misleading information dissemination. There is a genuine risk of predatory journals exploiting AI for swift but substandard article production, potentially inundating the market with low-quality research that compromises the scientific community’s credibility. The proliferation of false information in scientific articles poses a significant threat, undermining the integrity and progress of scientific research.

Several potential solutions exist to mitigate the risks associated with AI’s use in scientific article production. One approach involves developing AI algorithms specifically tailored for generating scientific articles. These algorithms could be trained on vast datasets of peer-reviewed, high-quality research to ensure the accuracy of generated information. Moreover, they could be programmed to flag potentially problematic content, such as references to unreliable sources, prompting researchers to conduct further review and verification. Another strategy entails enhancing AI systems’ ability to evaluate the authenticity and reliability of processed information. This could entail training AI on extensive datasets of high-quality scientific articles and employing techniques like cross-validation and peer review to ensure the generation of accurate and trustworthy results. Establishing stricter guidelines and regulations for AI use in scientific research is another avenue for mitigating risks. This could involve mandating researchers to disclose their use of AI in article production, implementing review processes to uphold certain quality and accuracy standards for AI-generated content, and penalizing non-compliance. Public education about AI’s limitations and potential dangers in scientific information reliance could also be beneficial in preventing misinformation dissemination and fostering better discernment between reliable and unreliable scientific sources. Funding agencies and academic institutions could contribute by providing training and resources to help researchers comprehend AI’s limitations.

Overall, addressing the risks associated with AI in scientific article production necessitates a multifaceted approach encompassing technical solutions, regulatory frameworks, and public awareness efforts. By implementing these measures, we can ensure responsible and effective AI use in the scientific realm. Researchers and policymakers must carefully consider the potential pitfalls of AI in scientific research and take proactive steps to mitigate these risks. Until AI can reliably produce accurate information, it should be employed judiciously in scientific endeavors. Thorough evaluation and validation of information provided by AI tools are imperative to uphold the integrity and credibility of scientific research⁵³.

Ethical Considerations in Utilizing AI in the Pharmaceutical Sector: As discussed earlier, it’s crucial to examine the ethical implications of AI utilization in this sector^54,55. One significant concern is AI’s potential role in making decisions impacting people’s health, like determining which drugs to develop, conducting clinical trials, and managing drug marketing and distribution. Another critical issue involves the possibility of bias in AI algorithms, leading to unequal access to medical care and unfair treatment of certain demographics, undermining principles of equality and justice. Additionally, AI’s integration in the pharmaceutical industry raises worries about job displacement due to automation, necessitating support for affected workers. Moreover, concerns arise regarding data privacy and security, as AI systems rely on extensive data, risking access or misuse of sensitive personal information, posing potential harm to individuals and companies’ reputations. Therefore, it’s imperative to collect and handle sensitive medical data in a manner respecting individuals’ privacy and complying with relevant regulations.

In summary, the ethical application of AI in the pharmaceutical domain demands careful deliberation and implementation of thoughtful strategies to address these concerns. This includes ensuring AI systems are trained on diverse and representative datasets, regularly auditing them for bias, and enforcing robust data privacy and security measures. By addressing these issues, the pharmaceutical industry can responsibly and ethically harness the potential of AI⁵⁶.

Artificial Intelligence in Pharmaceutical Marketing:

Given the growing intricacies of manufacturing processes and the escalating need for enhanced efficiency and product quality, contemporary manufacturing systems are endeavoring to impart human expertise to machines, continually evolving manufacturing practices. The integration of AI in manufacturing holds promising potential for the pharmaceutical sector. Tools like Computational Fluid Dynamics (CFD) leverage Reynolds-Averaged Navier-Stokes solvers technology to analyze the effects of agitation and stress levels in various equipment, such as stirred tanks, streamlining the automation of numerous pharmaceutical operations. Similarly, systems like direct numerical simulations and large eddy simulations employ sophisticated methods to address complex flow challenges in manufacturing.

The Innovative Chapter platform facilitates digital automation for molecule synthesis and manufacturing, employing diverse chemical codes and operating through a scripting language termed Chemical Assembly32. This platform has demonstrated success in the synthesis and production of medications like sildenafil, diphenhydramine hydrochloride, and rufinamide, yielding purity and efficiency levels akin to manual synthesis. AI technologies enable efficient estimation of granulation completion in granulators ranging from 25 to 600 liters. By employing technology and neuro-fuzzy logic, critical variables are correlated with their respective responses, yielding polynomial equations for predicting the requisite proportion of granulation fluid, optimal speed, and impeller diameter in both geometrically similar and dissimilar granulators⁵⁷.

Advantages of Artificial Intelligence:

1. Error Reduction: AI aids in minimizing errors and enhancing precision, particularly in space exploration where intelligent robots withstand harsh conditions.

2. Facilitates Exploration: AI is instrumental in sectors like mining and fuel exploration, as well as in oceanic exploration, overcoming errors introduced by humans.

3. Everyday Applications: From GPS navigation to predictive text on smartphones, AI simplifies daily tasks and assists in spell-checking.

4. Digital Assistants: Organizations leverage AI-driven digital assistants to streamline operations and decision-making without being influenced by human emotions.

5. Efficiency in Repetitive Tasks: Machines excel in multitasking and rapid analysis, adjusting parameters like speed and time as needed.

6. Medical Advancements: AI aids physicians in assessing patient conditions, analyzing medication effects, and provides training through surgical simulators.

7. Continuous Operation: Unlike humans, AI-powered machines can work tirelessly for extended periods without breaks or fatigue.

8. Technological Advancement: AI fuels innovation across various industries, contributing to the development of computational models, molecule discovery, and drug formulations.

9. Risk Mitigation: AI minimizes risks in hazardous environments such as fire stations, with repairable machine parts in case of mishaps.

10. Assistance: AI serves as round-the-clock aids for children and elders, acting as both educators and companions.

11. Versatility: Machines equipped with AI can perform diverse tasks efficiently without emotional constraints.

12. Early detection: AI-powered diagnostic tools can assist in the early detection of diseases like cancer, Alzheimer’s, diabetes, and cardiovascular conditions, potentially leading to earlier intervention and improved patient outcomes. These technologies help clinicians make more accurate diagnoses, detect diseases at earlier stages when treatment is more effective, and ultimately save lives⁵⁸.

Disadvantages of Artificial Intelligence:

1. Costly Implementation: The initial investment in AI is substantial, encompassing complex design, maintenance, and software updates, leading to prolonged and expensive R&D processes.

2. Limited Human Replication: While AI-equipped robots excel in tasks devoid of emotions, they may struggle with unfamiliar scenarios, lacking the ability to think and provide accurate responses.

3. Inability to Learn from Experience: Unlike humans, AI lacks the capacity to improve with experience, unable to discern hardworking individuals from non-workers.

4. Lack of Original Creativity: Machines with AI lack emotional and sensory capabilities, hindering their ability to think creatively or use intuition.

5. Potential Unemployment: Widespread AI adoption may lead to significant job displacement, impacting human creativity and work habits negatively^59,60.

CONCLUSION:

AI is revolutionizing drug delivery technologies, facilitating targeted, personalized, and adaptable therapies. By harnessing AI's capabilities in data analysis, pattern recognition, and optimization, pharmaceutical researchers and healthcare professionals can enhance drug effectiveness, minimize adverse effects, and elevate patient outcomes. AI-based approaches have transformed the field of pharmacokinetics and pharmacodynamics, offering numerous advantages over traditional experimental methods. AI models can predict pharmacokinetic parameters, simulate drug distribution and clearance within the body, and optimize drug dosage and administration routes. Computational methods employing AI for physiologically based pharmacokinetic (PBPK) models simplify model development and parameter optimization, reducing reliance on animal studies and human clinical trials. Computational pharmaceutics, empowered by AI and big data, revolutionizes the drug delivery process by introducing a more efficient, cost-effective, and data-driven approach. It facilitates the optimization of drug formulations, personalized therapies, regulatory compliance, and risk mitigation, ultimately enhancing drug manufacturing processes and improving patient outcomes. Overall, the integration of AI technologies holds immense promise for accelerating drug development, enhancing patient outcomes, and transforming the pharmaceutical industry, propelling its evolution from era 4.0 to era 5.0.

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Received on 08.04.2024 Modified on 04.07.2024

Asian J. Pharm. Tech. 2024; 14(3):237-246.

DOI: 10.52711/2231-5713.2024.00039

Organisation	Year of establishment	Location	Technology	Products and services
Standigm	2015	South Korea	AI- Core	Drug design
CytoReason	2016	Israel	AI and ML	Data driven drug design
Gnome Biologics	2016	Germany	Pattern Recognition and ML	Preclinical drug discovery
BullFrog AI	2017	USA	NLP	Advanced data analysis techniques
Causaly	2017	UK	NLP and ML	Causaly knowledge graph consisting of variety of data sources, including biomedical litrature and clinical trials and several sude effect database
Deep Cure	2018	USA	AI, Deep Learning	Discovery of small molecules therapeutics
Polaris QuantumBiotech	2020	USA	Quantum computing-driven search of large chemical datasets	Drug design plateform that can produce a drug blueprint