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Related Concept Videos

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Biopharmaceutical Factors Influencing Drug Product Design: Overview01:22

Biopharmaceutical Factors Influencing Drug Product Design: Overview

Rational drug product design integrates knowledge of the drug’s physicochemical properties, formulation components, manufacturing techniques, and intended route of administration. Each factor influences the drug’s performance, including how it is released, absorbed, and eliminated in the body.The physicochemical properties of a drug—such as solubility, stability, and particle size—affect its compatibility with excipients and the choice of dosage form. Excipients, though pharmacologically...
Drug Discovery: Overview01:26

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).
Pharmaceutical Poisoning: Potential Scenarios01:26

Pharmaceutical Poisoning: Potential Scenarios

Pharmaceutical poisoning can occur through various channels, impacting an estimated 2 million hospitalized patients in the U.S. annually with serious adverse drug responses. These scenarios encompass both therapeutic uses, such as drug toxicity, where even standard dosages can lead to severe central nervous system depression, and non-therapeutic exposures, including accidental ingestion by children, and environmental and occupational exposures.Unintentional poisonings often involve exploratory...
Pharmaceutical Alternatives: Excipients and Impurities-Related Therapeutic Nonequivalence01:19

Pharmaceutical Alternatives: Excipients and Impurities-Related Therapeutic Nonequivalence

Pharmaceutical products contain more than just the active drug; they also contain various excipients such as binders, solubilizers, stabilizers, preservatives, and other elements. In some cases, impurities or contaminants might be present. Traditionally, quality control in pharmaceuticals has primarily focused on the analysis of the active drug, often overlooking the impact of these additional components. The recent issue with heparin contamination by over-sulfated chondroitin sulfate, a...

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Related Experiment Videos

A review on artificial intelligence in pharmaceutical sciences: opportunities and challenges.

Vivek Kumar1, Saurav Kumar2, Ravi Kumar3

  • 1Kashi Institute of Pharmacy, MS 23KM, Varanasi - Prayagraj (Allahabad) Highway, Mirzamurad Varanasi Uttar Pradesh, India.

Drug Development and Industrial Pharmacy
|May 10, 2026
PubMed
Summary

Artificial intelligence (AI) is revolutionizing pharmaceutical sciences by accelerating drug development and personalizing medicine. Challenges in data and regulation remain, but AI integration promises more efficient and patient-centered healthcare.

Keywords:
Artificial intelligenceclinical trialsdrug discoverymachine learningpharmaceutical industrypharmacy

Related Experiment Videos

Area of Science:

  • Pharmaceutical Sciences
  • Biotechnology
  • Computational Biology

Background:

  • Artificial intelligence (AI) is increasingly impacting pharmaceutical research and development.
  • The integration of AI offers potential solutions to long-standing challenges in drug discovery and clinical trials.
  • Understanding the scope and implications of AI in pharmaceuticals is crucial for future advancements.

Purpose of the Study:

  • To systematically review the transformative impact of artificial intelligence (AI) on pharmaceutical sciences.
  • To explore how AI accelerates drug development, enhances clinical trials, and optimizes manufacturing.
  • To examine AI's role in personalized medicine and identify associated challenges.

Main Methods:

  • A comprehensive literature review synthesizing recent studies, industry reports, and regulatory guidelines.
  • Analysis of AI applications across drug discovery, clinical trials, pharmacovigilance, manufacturing, and pharmacy education.
  • Critical examination of barriers including data quality, privacy, explainability, and regulatory hurdles.

Main Results:

  • AI significantly accelerates pharmaceutical R&D, including target identification, lead optimization, and ADMET prediction, leading to faster, cost-effective drug development.
  • AI enables advancements in personalized medicine, improving patient outcomes through individualized data analysis.
  • Persistent challenges include data interpretation, privacy concerns, and navigating the evolving regulatory landscape.

Conclusions:

  • Ongoing AI advancements and evolving regulations are poised to revolutionize pharmaceutical science, enabling efficient, predictive, and patient-centered healthcare.
  • Successful integration requires addressing challenges like data quality and explainability, and fostering stakeholder collaboration.
  • AI promises safer, faster, and more precise drug delivery, benefiting clinicians, researchers, and students.