Artificial Intelligence Powered Pharmaceutical Innovation

Artificial intelligence is a subfield of computer science that uses symbolic programming to assist humans in solving problems. AI has become a national addiction in the 20th century. Real instances of artificial intelligence that we currently use in our daily lives like Chat GPT, META AI, etc. Several leading pharmaceutical companies, including Pfizer, Novartis, Johnson & Johnson Science, and others are utilizing artificial intelligence. AI has been the leading competitor of innovation, enabling previously unthinkable advancements in industries including finance, healthcare, manufacturing, and transportation.  ^[1]

According to John McCarthy, the Father of Artificial Intelligence, it is ‘‘The science and engineering of making intelligent machine”. The goal of the AI system is to develop a system capable of solving complex problems in ways similar to human logic and reasoning. This review aims to investigate the diverse applications of artificial intelligence in the field of drug discovery, and pharmacy practice, identify research gaps and problems, and suggest future path.  ^{[1, 3]}

History Of Artificial Intelligence ^[2]

Alan Turing’s seminal work, “Computing Machinery and Intelligence,” published in 1950, marked the beginning of the artificial intelligence. In 2004, Father of artificial intelligence,

John McCarthy.

• 1950s: The Dartmouth Summer Research Project on Artificial Intelligence (1956) is considered the birthplace of AI. Attendees like John McCarthy, Marvin Minsky, and Nathaniel Rochester explored AI's possibilities.
• 1951: Alan Turing proposed the Turing Test, a measure of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human.
• 1956: The term "Artificial Intelligence" was coined by John McCarthy.
• 1960: Rule-based expert systems were developed, mimicking human decision-making processes.
• 1970s-1980s: AI research experienced a decline in funding, known as the "AI winter."
• 1987: The market for specialized LISP-based hardware collapsed due to cheaper and more accessible competitors that could run LISP software, including those offered by IBM and Apple. This caused many specialized LISP companies to fail as the technology was now easily accessible.
• 1990s-2000s: AI resurgence began with advancements in machine learning, natural 2                                                                                                                 language processing, and computer vision.
• 2020: OpenAI started beta testing GPT-3, a model that uses Deep Learning to create code, poetry, and other such language and writing tasks. While not the first of its kind, it is the first that creates content almost indistinguishable from those created by humans.
• 2021: OpenAI developed DALL-E, which can process and understand images enough to produce accurate captions, moving AI one step closer to understanding the visual world.

Terminologies Used in AI ^{[3, 4]}

Scope And Applications Of AI ^[4]

The scope of Artificial Intelligence is vast and rapidly arising field that is changing many parts of our lives and industries. The following are some of the main areas where AI is having a big impact:

1. Healthcare: Diagnosis, drug discovery, personalized medicine, and medical imaging analysis.
2. Finance: Fraud detection, credit scoring, portfolio management, and algorithmic trading.
3. Education: Personalized learning, intelligent tutoring systems, and automated grading.
4. Transportation: Autonomous vehicles, traffic management, and route optimization.
5. Manufacturing: Predictive maintenance, quality control, and supply chain optimization.
6. Customer Service: Chat bots, virtual assistants, and sentiment analysis.
7. Cybersecurity: Threat detection, incident response, and vulnerability assessment.

Artificial Intelligence in The Field of Drug Discovery

Animal studies have long been a foundation of scientific research, but growing concerns about animal welfare, ethics, and the limitations of animal models have sparked a search for alternative methods. By embracing AI as an alternative to animal studies, researchers can promote more humane, efficient, and effective scientific research. This module explores how new technologies like organs-on-a- chip and computer simulations are reducing the need for animal testing in drug development, making the pharmaceutical industry more ethical and efficient [6, 10].

How Technology Reduces Animal Testing?

New alternative methods instead of animal testing incorporate the 3R principles:

• Replacing: Substituting animal models with non-animal systems or with less developed animals. 
• Reducing: Decreasing the number of animals required while still achieving testing objectives. 
• Refining: An alternative testing method enhances animal well-being. [6, 10]

Examples For Non-Animal Model Alterntives:

5. Organ-On-A-Chip (OOC)

An Organ-on-a-Chip (OOC) is a microfluidic cell culture chip that simulates the activities, mechanics and physiological response of entire organs and organ systems. It's a tiny, artificial replica of an organ, like a heart or lung, on a chip, allowing researchers to:

• Study organ function and behaviour
• Test drug effects and toxicity
• Reduce animal testing and clinical trials ^[6,10]      

5. Organoids

Organoids are 3D cellular structures grown in a lab that mimic the architecture and function   of native tissues. These are used to study organ development, disease modelling, and drug discovery. ^{[6, 10]}

5. In Silico Trials 

In silico trials, also known as computer simulations or virtual trials can mimic clinical trials, reducing the need for animal testing in drug development and toxicology.  They allow researchers to: 

• Simulate clinical trials and drug interaction.
• Model disease progression and treatment responses. ^[7,11]

5. Personalized Medicines 

AI can help develop personalized treatment plans for humans, reducing the need for animal testing to predict human responses. AI can:

• Identify the most effective treatments and minimize side effects
• Develop targeted therapies for specific patient populations
• Optimize drug dosing and delivery for individual patients
• Improve patient outcomes and reduce healthcare costs ^[7,11,12,13]

AI In Drug Discovery: Overcoming Key Challenges

• AI requires high-quality, diverse, and large datasets, which can be difficult to obtain in drug discovery.
• AI models can be complex, making it challenging to understand their decision-making processes.
• AI may require significant changes to existing drug discovery workflows and infrastructure.
• Requires specialized expertise in both AI and drug discovery.
• AI may struggle to generate novel hypothesis or ideas. ^[12,13]

Strategies To Overcome the Challenges

5. Human-AI collaboration:

By Combining AI's predictive power with human expertise and judgment.

5. Expertise and training:

By providing interdisciplinary training and developing specialized AI-drug discovery expertise.

5. Continuous learning and improvement:

Regularly updating and refining AI models and strategies.

5. Regulatory engagement:

By collaborating with regulatory agencies to establish guidelines and acceptance criteria.

5. Workflow integration:

By seamlessly integrating AI into existing drug discovery workflows. ^{[12, 13]}

AI in the field of pharmacy practice

In the rapidly evolving landscape of healthcare, Artificial Intelligence (AI) is emerging as a transformative force in pharmacy practice. AI has the potential to significantly improve medication management and patient care. Imagine a world where pharmacy professionals have real-time access to predict patient needs, optimize medication regimens, and enhance clinical outcomes, AI is bringing this vision to life and changing how pharmacist communicate with both patients and data.  AI's capabilities extend far beyond simple automation; it empowers pharmacists to harness the power of data analytics, predictive modelling, and machine learning. From precision medicine to personalized patient care, AI tools are streamlining medication management, improving accuracy in drug interactions, and facilitating more informed clinical decisions. By integrating AI into pharmacy practice, we’re not just enhancing efficiency we’re elevating the quality of care and paving the way for a more responsive and adaptive healthcare system.  AI allows for greater collaboration between different healthcare services provided to a single patient.  AI models have been developed to predict and detect adverse drug events, assist clinical decision support systems with medication-related decisions, Diseases diagnosis using AI, automate dispensing processes in community pharmacies, optimize medication dosages, detect drug-drug interactions, improve adherence through smart technologies, detect and prevent medication errors. ^{[6, 8]}

Applications Of AI In Pharmacy Practice

• Role of AI in detecting the ADR OF Medicines

Artificial Intelligence (AI) is playing an increasingly pivotal role in the detection, prediction, and management of Adverse Drug Reactions (ADRs), which are unintended and harmful effects resulting from the use of medications. Here’s how AI is revolutionizing the field:

5. Predictive Analytics

AI algorithms can analyse vast amounts of data from electronic health records, clinical trials, and patient reports to identify patterns that may indicate potential ADRs. By leveraging machine learning models, AI can predict which patients are at higher risk for specific reactions based on their genetic, demographic, and clinical profiles. This predictive capability enables pre-emptive interventions, helping to avoid adverse outcomes before they occur.

5. Real-Time Monitoring

AI-powered systems continuously monitor patient data in real-time to detect signs of ADRs as they happen. These systems analyse various sources of data, such as electronic health records (EHRs), wearable devices, and patient-reported outcomes, to identify deviations from normal health patterns that may signal an ADR. Early detection allows for timely adjustments to treatment plans, improving patient safety.

5. Drug Development and Testing

During the drug development phase, AI can analyse preclinical and clinical data to predict potential ADRs before a drug reaches the market. This capability accelerates the identification of safety concerns and helps refine drug formulations, leading to safer medications.

5. Patient Education and Engagement

AI can also play a role in educating patients about potential ADRs and empowering them to report adverse reactions. Chat bots and virtual health assistants can provide patients with information about their medications, encourage them to report side effects, and guide them through the reporting process. ^[8]

• Clinical Decision Support

        Artificial Intelligence (AI) is revolutionizing Clinical Decision Support (CDS) by               enhancing the accuracy, efficiency, and effectiveness of medical decision-making. Here’s how AI is making a significant impact in this critical area of healthcare:

5. Enhanced Diagnostic Accuracy

AI algorithms, particularly those based on machine learning and deep learning, can analyse medical images (e.g., X-rays, MRIs, CT scans) with high precision, often matching or exceeding human radiologists' performance. AI systems can identify patterns and anomalies that may be missed by the human eye, assisting in early and accurate diagnosis of conditions such as cancer, cardiovascular diseases, and neurological disorders.

5. Personalized Treatment Recommendations

AI systems can analyse patient-specific data, including genetic information, medical history, and current health status, to provide personalized treatment recommendations. For instance, in oncology, AI can suggest targeted therapies based on the genetic profile of a tumour, optimizing treatment effectiveness and minimizing side effects.

5. Clinical Workflow Optimization

AI enhances clinical workflows by automating routine tasks such as data entry, appointment scheduling, and medication management. AI-powered tools can streamline administrative processes, reducing the burden on healthcare providers and allowing them to focus more on direct patient care. This efficiency can lead to better patient outcomes and improved healthcare delivery.

5. Evidence-Based Guidelines and Protocols

AI can help implement and adhere to evidence-based clinical guidelines and protocols by analysing current medical literature and patient data. AI systems can ensure that treatment decisions align with the latest research and best practices, improving the overall quality of care.

• Disease Diagnosis

5. Early Detection: AI tools can help in early detection of diseases like cancer, where early intervention is crucial. For example, AI algorithms can analyse mammograms to detect early signs of breast cancer, often identifying issues at a stage when treatment options are more effective
5. Genetic Analysis: AI algorithms can interpret complex genetic data to identify mutations and variations associated with specific diseases. This capability supports personalized medicine by tailoring treatment plans based on an individual’s genetic profile, enhancing treatment efficacy and minimizing adverse effects.
5. Targeted Therapies: In oncology, for instance, AI helps in identifying genetic markers that can predict how well a patient will respond to certain targeted therapies.This leads to more personalized and effective treatment plans.
5. Lifestyle Recommendations: AI can provide personalized recommendations for lifestyle modifications, such as diet and exercise, based on individual patient data and cardiovascular health metrics.

• Dose Recommendation

AI enhances dose recommendation by analysing patient-specific data—such as age, weight, genetic profile, and medical history—along with real-time response to treatment. Machine learning algorithms process this data to predict the optimal dosage of medications, ensuring both efficacy and safety. AI systems can adjust dosages dynamically based on ongoing patient feedback and health metrics, minimizing the risk of adverse effects and improving therapeutic outcomes. This personalized approach helps clinicians tailor treatments more precisely to individual needs.

• Medication Error Identification

The Food and Drug Administration (FDA) receives over 100,000 reports from the United States each year regarding suspected medication errors (MEs). Prescription errors occur at rates ranging from 0.3 to 9.1% in European hospitals, while dispensing errors occur at rates ranging from 1.6 to 2.1%. According to reports, a comprehensive and systematic approaches to patient safety can prevent up to 70.2% of ME-related harm. Implementation of electronic prescription systems, robust medication error surveillance, and the use of barcode medication administration systems are promising strategies for reducing MEs occurrence.

5. IBM Watson for Health

IBM Watson analyses patient records, including medication history and lab results, to identify potential medication errors. By cross-referencing prescribed drugs with known drug interactions and patient-specific factors, Watson alerts healthcare providers to potential issues such as contraindications and incorrect dosages. ^{[8, 9, 10, 15]}