Bairagee / Panchawat / Sharma Technical Advances in Pharmacy: An Overview

1. Auflage 2025
ISBN: 979-8-89881-237-9
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 250 Seiten

Technical Advances in Pharmacy: An Overview examines the convergence of digital transformation, automation, and data intelligence in revolutionizing drug discovery, formulation, and healthcare delivery Across twelve in-depth chapters the book presents a forward-looking exploration of how emerging technologies are reshaping the landscape of pharmaceutical sciences. The book also covers cutting-edge developments such as artificial intelligence in drug discovery, pharmacogenomics and personalized medicine, blockchain in pharmaceutical supply chains, 3D printing of dosage forms, tele pharmacy, data analytics, and automation in pharmacy practice. By integrating computational modelling with experimental techniques, the text highlights the translational potential of modern technologies in enhancing drug development, patient adherence, and clinical outcomes. Serving as both a reference and a roadmap, this volume bridges traditional pharmaceutical foundations with next-generation innovations, offering readers an insightful perspective on the technological evolution shaping the future of pharmacy. Key Features Examines the role of artificial intelligence, pharmacogenomics, and data analytics in modern drug discovery. Explores blockchain, automation, and robotics as enablers of efficient and transparent pharmacy systems. Integrates computational modeling with experimental approaches for enhanced drug formulation and testing. Highlights 3D printing, telepharmacy, and RegTech as transformative tools in pharmaceutical innovation. Bridges classical pharmaceutical sciences with emerging digital and biotechnological advancements.

Autoren/Hrsg.

Bairagee, Deepika

Panchawat, Sunita

Sharma, Neeraj

Saraogi, Gaurav K

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Pharmakologie, Toxikologie

Leseproben

Artificial Intelligence (AI) and Drug Discovery

Deenanath Jhade1, Steward Mudenda2, Neeraj Sharma3, *

1 St. Wilfred’s Institute of Pharmacy, University of Mumbai, Mumbai, Maharashtra, India

2 Department of Pharmacy, University of Zambia, Lusaka, Zambia

3 School of Pharmacy, IPS Academy, Indore, Madhya Pradesh, India

Abstract

A potent tool that leverages human knowledge to solve complicated problems efficiently is artificial intelligence (AI). Drug research, pharmaceutical dosage formulation, and testing are at a turning point because of remarkable developments in AI and machine learning. Through the use of AI algorithms that examine vast amounts of biological data, such as proteomics and genomics, researchers can identify disease-associated targets and predict how they will interact with possible therapeutic options. This enables a more effective and precise approach to drug discovery, which in turn raises the possibility of drug approval. AI may also help reduce development expenses by streamlining research and development procedures. In addition to aiding in trial design, machine learning algorithms can predict the pharmacokinetics and toxicity of potential drugs. By prioritizing and optimizing the lead compounds, this capacity reduces the need for expensive and time-consuming animal testing. AI algorithms that evaluate actual patient data can support personalized medical strategies and improve patient adherence and treatment outcomes. This comprehensive overview examines the application of AI in drug discovery, dosage form design for drug delivery, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) research. This chapter highlights the advantages and disadvantages of certain AI-based techniques used in pharmaceutical technologies. However, the pharmaceutical industry's ongoing investment and research present intriguing opportunities to improve patient care and drug development.

Keywords: Artificial intelligence (AI), Dosage form testing, Drug discovery, Machine learning, Pharmacodynamics, Pharmacokinetics, Physiologically-based pharmacokinetic (PBPK), Quantitative structure-activity relationship (QSAR).

* Corresponding author Neeraj Sharma: School of Pharmacy, IPS Academy, Indore, Madhya Pradesh, India; E-mail: [e-mail]

INTRODUCTION

Artificial Intelligence (AI) refers to the simulation of human intelligence using computer systems to perform tasks, such as learning, reasoning, problem-solving, and decision-making. In healthcare, AI encompasses a wide range of applications,

from diagnostic tools to usage planning and from patient management to pharmaceutical research. The use of AI has expanded significantly, driven by advancements in machine learning, big data analytics, and computational power [1, 2]. AI can be used to analyze vast datasets, identify patterns, and provide actionable insights, making it an invaluable tool for addressing complex healthcare challenges. Its scope in healthcare extends beyond diagnostics and imaging, delving into areas, such as drug discovery, personalized medicine, and advanced drug delivery systems, where it aids in designing efficient, patient-centric solutions [3-5].

Overview of Drug Delivery Systems and Challenges in Discovery and Development

Despite significant advancements, the discovery and development of novel drug delivery systems remains fraught with numerous challenges. One of the primary obstacles lies in the complexity of the formulation design. Designing an effective drug delivery system requires understanding and predicting the interactions between drugs, polymers, and other excipients. These interactions are influenced by multiple variables, including chemical composition, concentration, and environmental conditions, making them a multifactorial problem. This process often requires extensive experimentation, trial and error, and iterative refinement, which can be resource-intensive and time-consuming [6-8].

Another major challenge is the high cost and time-consuming nature of the development process. Research, development, and testing of new drug delivery systems require significant financial and temporal investment. From the initial conceptualization to preclinical and clinical trials, each phase presents a high risk of failure. This not only delays the availability of innovative therapies but also poses financial risks to pharmaceutical companies, potentially discouraging investment in novel delivery technologies.

Biological variability adds another complexity to drug delivery. Human physiology is inherently variable, with differences in enzyme levels, metabolic rates, and disease states that influence drug efficacy. For example, a formulation that works well in one individual may not achieve the same therapeutic effect owing to differences in absorption, distribution, metabolism, or excretion. This variability complicates the development of universally effective drug delivery systems, underscoring the need for personalized approaches to treatment.

Additionally, regulatory hurdles impede progress in this field. New drug delivery systems must meet stringent safety and efficacy standards set by regulatory agencies before reaching the market. The approval process often involves exhaus- tive documentation, rigorous testing, and compliance with numerous guidelines, which can delay innovation and increase cost.

These challenges underscore the urgent need for innovative tools and methodologies to streamline the discovery process, optimize formulations, and predict outcomes more effectively. Integrating technologies, such as artificial intelligence, offers promising avenues to address these obstacles, thereby transforming the landscape of drug delivery research and development [9, 10].

Role of AI in Transforming Traditional Drug Delivery Research

Drug delivery systems are intended to transport therapeutics to their site of action in the body, enhance efficacy, and minimize side effects. Traditional approaches, such as tablets or injections, often suffer from limitations, such as poor bioavailability, systemic toxicity, and lack of targeted delivery. Modern approaches, including nanoparticles, liposomes, and transdermal patches, aim to overcome these issues by improving the controlled release, stability, and targeting mechanisms.

AI is revolutionizing traditional drug delivery research by offering powerful tools to address these challenges. Machine learning algorithms, for instance, can examine large datasets of drug properties, excipient combinations, and biological interactions to identify optimal formulations. Predictive representations can simulate drug release kinetics, bioavailability, and pharmacokinetics, significantly reducing the need for extensive in-vitro and in-vivo testing [11].

In addition, AI enables the design of personalized drug delivery systems by integrating patient-specific data, such as genetic information and disease profiles, to tailor treatment. AI-powered in-silico models facilitate virtual screening and rapid prototyping, thereby accelerating the development of innovative delivery platforms. Furthermore, AI-driven automation enhances precision in manufacturing and quality control, ensuring consistency in the delivery of drugs.

By influencing AI, investigators can overcome traditional bottlenecks, reduce costs, and enhance the accuracy of drug delivery systems, ultimately improving patient outcomes. This transformative potential highlights the importance of integrating AI into the drug discovery pipeline [1, 12].

HISTORICAL PERSPECTIVE ON DRUG DELIVERY SYSTEMS: FROM CONVENTIONAL METHODS TO ADVANCED CONTROLLED RELEASE

The evolution of drug delivery systems has been marked by a progressive shift from simple conventional methods to highly sophisticated controlled-release technologies. Initially, drug delivery primarily relied on basic oral and injectable formulations. Tablets, capsules, and intravenous injections have been designed to deliver therapeutic agents; however, they often suffer from limitations, such as poor bioavailability, rapid clearance from the body, and systemic side effects. These limitations stem from the inability to control the release rate or target specific sites within the body [13].

The development of sustained-release formulations in the mid-20th century marked a significant advancement, enabling the gradual release of drugs over extended periods of time. This innovation enhances patient compliance and therapeutic efficacy by reducing the frequency of dosing. Technologies, such as transdermal patches and depot injections, have been employed, enabling more consistent drug levels in the bloodstream.

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