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- Herausgeber: Bentham Science Publishers
- Kategorie: Lebensstil
- Sprache: Englisch
Computer-Aided Drug Discovery Methods: A Brief Introduction explores the cutting-edge field at the intersection of computational science and medicinal chemistry. This comprehensive volume navigates from foundational concepts to advanced methodologies, illuminating how computational tools accelerate the discovery of new therapeutics.
Beginning with an overview of drug discovery principles, the book explains topics such as pharmacophore modeling, molecular dynamics simulations, and molecular docking. It discusses the application of density functional theory and the role of artificial intelligence in therapeutic development, showcasing successful case studies and innovations in COVID-19 research.
Ideal for undergraduate and graduate students, as well as researchers in academia and industry, this book serves as a vital resource in understanding the complex landscape of modern drug discovery. It emphasizes the synergy between computational methods and experimental validation, shaping the future of pharmaceutical sciences toward more effective and targeted therapies.
Readership
Undergraduate/Graduate and Research
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Seitenzahl: 205
Veröffentlichungsjahr: 2024
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PREFACE
In the ever-evolving landscape of pharmaceutical research and development, the convergence of computational science and medicinal chemistry has ushered in a new era of innovation: Computer-Aided Drug Discovery (CADD). This exciting and rapidly advancing field represents a paradigm shift in identifying, designing, and optimizing novel therapeutics.
The journey from molecular target identification to clinical drug candidates is intricate and challenging, marked by complicated molecular interactions, complex biological systems, and an unyielding quest for efficacy and safety. In this intricate relationship between science and technology, CADD emerges as a guiding light, illuminating previously obscured pathways and accelerating drug discovery.
In this comprehensive volume, we embark on an exploration of the multifaceted world of Computer-Aided Drug Discovery. The chapters herein span a spectrum of methodologies, each contributing to our understanding of molecular interactions, predictive modeling, and rational design. From virtual screening and molecular docking to molecular dynamics simulations and machine learning algorithms, the tools at our disposal are diverse and robust.
Through the pages of this book, we delve into the intricacies of ligand-receptor interactions, binding free energy calculations, and the role of quantum mechanics in drug discovery. We examine the nuances of structure-based and ligand-based approaches while uncovering the potential of artificial intelligence and deep learning in unraveling the mysteries of molecular recognition.
However, as we navigate this fascinating domain, it is crucial to remember that CADD is not a panacea but a complementary force that enhances the ingenuity of medicinal chemists and biologists. The synergy between computational methods and experimental validation is the cornerstone of successful drug discovery.
As we venture further into the frontiers of drug discovery, the collective efforts of researchers, practitioners, and pioneers in CADD are reshaping the landscape of pharmaceutical science. The promise of more efficient, cost-effective, and targeted therapies is becoming a reality driven by the fusion of human intellect and computational power.
This book serves as both a reference and an inspiration, guiding us toward a future where Computer-Aided Drug Discovery is an essential pillar of modern healthcare, enhancing our ability to alleviate suffering and improve the human condition.
Onward to the future of drug discovery.
Drug Discovery
Manos C. Vlasiou1Abstract
Drug discovery is a complex process involving target identification, lead generation, and clinical development. Recent breakthroughs in genomics and AI-driven approaches have expedited target identification. Rational drug design and advanced chemistry techniques have improved lead compound optimization—preclinical testing benefits from organ-on-a-chip systems and 3D cell culture models. Clinical development is enhanced by personalized medicine and innovative trial designs. Across all stages, big data, machine learning, and AI play pivotal roles in data analysis and candidate selection. Collaboration between academia, industry, and regulators fosters a more efficient drug development ecosystem. These advancements offer promising prospects for tackling challenging diseases and enhancing global healthcare.
INTRODUCTION
Drug discovery is a complex and dynamic process involving identifying and developing novel therapeutic agents to combat human diseases. It encompasses a multidisciplinary approach, integrating biology, chemistry, pharmacology, and computational sciences [1]. Here, we explore the stages of drug discovery, including target identification, lead generation, lead optimization, and preclinical and clinical development, and highlight the challenges and advancements in this field. Target and lead identification are critical stages in drug discovery, laying the foundation for developing novel therapeutic agents. Target identification involves identifying and validating a specific molecule or pathway implicated in a disease.
In contrast, lead identification focuses on discovering compounds that interact with the target and show potential therapeutic activity [2]. We will now explore the methodologies, challenges, and advancements in target and lead identification, highlighting their importance in pursuing effective treatments.
Drug solubility is crucial in pharmaceutical development, directly influencing drug bioavailability, formulation, and therapeutic efficacy. Poor solubility remains
a significant challenge in drug discovery and formulation, limiting the successful delivery of many potential therapeutic agents. We will shed some light on the significance of drug solubility, the factors affecting solubility, and the strategies employed to enhance solubility and improve drug formulations.
Drug likeness is a concept that serves as a guiding principle in drug design and discovery, aiming to identify compounds with properties favourable for their development into safe and effective medications. It involves the evaluation of a molecule's physicochemical and structural characteristics against a set of desirable attributes to assess its potential as a drug candidate [3]. The significance of drug-likeness, the factors influencing it, and its role in optimizing the success of drug development will be discussed.
Drug databases are pivotal in pharmaceutical research and development, providing comprehensive and structured repositories of drug-related information [4]. They are invaluable resources for drug discovery, development, and clinical decision-making.
ADME is an acronym that represents the four essential processes a drug undergoes within the body: absorption, distribution, metabolism, and excretion [5]. These processes collectively determine a drug's pharmacokinetic profile, influencing its bioavailability, distribution to target tissues, metabolism, and elimination. We will also explore the significance of the drug ADME, the critical processes involved, and their implications for drug development and therapeutic outcomes [6].
Introduction to Drug Discovery
Target Identification
The first crucial step in drug discovery is identifying a target, a specific protein, enzyme, receptor, or genetic component involved in a disease pathway. Advances in genomics, proteomics, and bioinformatics have revolutionized target identification, enabling researchers to uncover new therapeutic targets and understand disease mechanisms at a molecular level [7]. Target validation, utilizing various experimental and computational techniques, is essential to establish the significance and feasibility of the target for drug intervention.
Lead Generation
Once a target is identified, the next step is to find lead compounds that interact with the target and exhibit potential therapeutic activity. Lead generation involves screening large libraries of compounds, either through high-throughput screening (HTS) or virtual screening methods. HTS involves testing thousands or even millions of combinations for their ability to bind to the target and elicit the desired biological response [8]. Virtual screening, on the other hand, employs computational techniques to screen and prioritize compounds based on their predicted interactions with the target.
Lead Optimization
After obtaining promising lead compounds, the focus shifts to lead optimization, which aims to enhance the compounds' potency, selectivity, and pharmacokinetic properties. Medicinal chemists modify the chemical structure of lead compounds through iterative rounds of synthesis, testing, and structure-activity relationship (SAR) studies [9]. The goal is to optimize the balance between efficacy, safety, and drug-like properties, guided by insights gained from molecular modelling, QSAR methods, and computational chemistry tools.
Preclinical Development
In the preclinical development stage, lead compounds undergo rigorous testing to assess their safety, efficacy, and pharmacokinetic profiles before progressing to clinical trials. Preclinical studies involve in vitro experiments, animal models, and toxicology assessments to evaluate the compound's pharmacological effects, potential side effects, and overall toxicity profile [10]. These studies provide crucial data for determining the candidate compound's dosage range, formulation, and possible therapeutic indications.
Clinical Development
If a lead compound successfully passes the preclinical evaluation, it progresses to clinical development, which involves testing the combination in humans through clinical trials. Clinical trials are conducted in phases, starting with Phase I, which focuses on safety and dosage determination, followed by Phase II and Phase III trials that assess efficacy, side effects, and comparative effectiveness against existing treatments [11]. These trials involve large patient populations and are tightly regulated to ensure patient safety and ethical conduct.
Regulatory Approval
The drug development process enters the regulatory phase upon completion of clinical trials. Pharmaceutical companies compile extensive data from preclinical and clinical studies and information on manufacturing processes, quality control, and safety measures to submit a New Drug Application (NDA) or equivalent to regulatory authorities such as the Food and Drug Administration (FDA) [12]. Regulatory agencies review the data and decide on the drug's approval, labelling, and post-marketing surveillance requirements.
Post-Marketing Surveillance
Once a drug is approved and enters the market, post-marketing surveillance becomes crucial to monitor the drug's safety and effectiveness in real-world patient populations. Adverse events and unexpected side effects may surface, necessitating continuous monitoring, pharmacovigilance, and periodic reassessment of the drug's risk-benefit profile. Post-marketing studies and ongoing clinical trials further contribute to understanding the drug's long-term effects and potential new indications.
Challenges and Advancements
Drug discovery faces numerous challenges, including a high failure rate, lengthy timelines, and escalating costs. Researchers constantly strive to overcome these challenges through technological advancements, automation, data analysis, and collaboration [13]. Emerging fields such as pharmacogenomics, artificial intelligence, and personalized medicine offer new avenues for targeted and efficient drug discovery [14]. Additionally, repurposing existing drugs and exploring natural products and biologics provide alternative strategies for identifying therapeutics.
Target and Lead Identification: Unveiling the Path to Therapeutic Success
Target Identification
Target identification involves identifying and validating a specific molecule or pathway that plays a crucial role in the development or progression of a disease. This stage relies on a multidisciplinary approach, integrating genomics, proteomics, bioinformatics, and systems biology. Several strategies are employed in target identification:
Genetic Approaches
Genetic approaches involve studying genetic variations and mutations associated with diseases. Genome-wide association studies (GWAS), next-generation sequencing, and functional genomics provide insights into disease-related genes and their impact on disease pathogenesis. By focusing on genes directly implicated in disease phenotypes, researchers can identify potential drug targets through these approaches.
Proteomic Approaches
Proteomic approaches aim to understand the functions and interactions of proteins within cellular pathways. Techniques such as mass spectrometry, protein-protein interaction studies, and protein profiling enable the identification of disease-associated proteins [15]. Researchers can uncover potential drug targets and gain insights into disease mechanisms by investigating protein expression patterns and post-translational modifications.
Chemical Biology Approaches
Chemical biology approaches make use of small molecules, probes, and chemical libraries to study biological systems. Chemical screenings, high-throughput (HTS), and fragment-based screening identify molecules interacting with specific targets or pathways [16]. These approaches can reveal new drug targets by identifying compounds that modulate disease-relevant phenotypes or protein activities.
