Converging Pharmacy Science and Engineering in Computational Drug Discovery: Interdisciplinary Drug Discovery

Converging Pharmacy Science and Engineering in Computational Drug Discovery: Interdisciplinary Drug Discovery

DOI: 10.4018/979-8-3693-2897-2.ch001
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Abstract

This chapter provides an in-depth exploration of the intersection between pharmacy science and engineering within computational drug discovery. It covers interdisciplinary collaborations, computational chemistry, bioinformatics, engineering principles, target identification, lead optimization, machine learning, and personalized medicine. Additionally, it examines the integration of experimental and computational data, addresses various challenges encountered, and offers insights into the future directions of computational drug discovery.
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Foundations Of Computational Drug Discovery

Drug Discovery Pipeline

The drug discovery process is a lengthy and complex journey that typically spans over a decade and involves substantial financial investments. This process can be broadly divided into several stages, each with its own set of challenges and bottlenecks.

Overview of the Traditional Drug Discovery Process

  • 1.

    Target Identification and Validation:

The first step in the drug discovery process is to identify and validate a biological target, such as a protein or a cellular pathway, that is implicated in a specific disease. Targets can be identified through various approaches, including genomics, proteomics, and bioinformatics analyses, as well as traditional biochemical and pharmacological studies.

  • 2.

    Lead Identification and Optimization:

Once a target has been identified and validated, researchers embark on the search for small molecules or biological entities (e.g., antibodies, peptides) that can modulate the target's activity. This process, known as lead identification, typically involves high-throughput screening of large chemical libraries or computational techniques like virtual screening.

Promising lead compounds are then subjected to an iterative process of optimization, where their chemical structures are systematically modified to improve their potency, selectivity, and pharmacokinetic properties.

  • 3.

    Preclinical Studies:

Optimized lead compounds are extensively evaluated in preclinical studies to assess their safety and efficacy profiles. These studies involve in vitro assays, such as cell-based assays and biochemical assays, as well as in vivo studies using animal models of the disease.

Preclinical studies also assess the pharmacokinetic and pharmacodynamic properties of the compounds, including their absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles.

  • 4.

    Clinical Trials:

Compounds that demonstrate promising results in preclinical studies proceed to clinical trials, which involve testing the potential drug in human subjects. Clinical trials are typically divided into three phases:

  • -

    Phase I: Evaluates the safety, tolerability, and pharmacokinetics of the drug in healthy volunteers.

  • -

    Phase II: Assesses the efficacy and optimal dosage of the drug in a small group of patients with the targeted disease.

  • -

    Phase III: Involves large-scale studies to confirm the drug's efficacy and monitor its long-term safety in a larger patient population.

5. Regulatory Approval and Commercialization:

If the clinical trials are successful, the pharmaceutical company must submit a comprehensive application to regulatory agencies, such as the Food and Drug Administration (FDA) or the European Medicines Agency (EMA), for marketing approval. This process involves a rigorous review of the drug's safety, efficacy, and quality data.

Upon approval, the drug can be manufactured and marketed for the approved indication.

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