Microorganisms are the primary triggers of various inflammatory diseases in the human body. Diseases such as bronchitis, otitis media, pneumonia, conjunctivitis, cystitis, endometritis, and infections of the fallopian tubes and ovaries are routinely treated with antimicrobial agents in global medical protocols. Bacterial infections like chlamydia, streptodermia, scarlet fever, meningitis, and tuberculosis cannot be treated without the application of antimicrobial therapies. Likewise, viral conditions such as herpes, chickenpox, hepatitis C and B, and HIV are now addressed using antiviral treatments. Antibiotics are used to treat fungal infections of the skin, mucous membranes, and nails, as well as systemic mycoses. For protozoal diseases such as giardiasis, amoebic dysentery, trichomoniasis, malaria, and toxoplasmosis, antiprotozoal drugs are prescribed. These therapeutic agents target a wide range of pathogens, including bacteria, viruses, fungi, and protozoa. This article offers a succinct review of various antimicrobial drugs and provides an in-depth analysis of the Russian antimicrobial drug market.
Artificial intelligence is increasingly being positioned as a transformative tool in pharmaceutical formulation and process development because it can model complex relationships among molecular properties, excipient behaviour, formulation variables, processing conditions, and product performance. Machine learning, deep learning, hybrid modelling, and optimisation algorithms are now used to support decisions that were previously dominated by empirical screening and expert judgement. Despite this promise, the practical translation of artificial intelligence into pharmaceutical development remains uneven. Many models demonstrate high retrospective accuracy but provide limited mechanistic insight, weak interpretability, and uncertain relevance when moved beyond the specific datasets, formulations, equipment, or scales on which they were trained. This critical review examines artificial intelligence in pharmaceutical formulation and process development through three linked lenses: explainability, transferability, and regulatory trust. It argues that these issues are not secondary implementation details but core determinants of whether artificial intelligence can become credible within quality-driven pharmaceutical development. The analysis shows that artificial intelligence can support formulation and process understanding only when predictive performance is accompanied by transparent reasoning, domain-aware validation, lifecycle governance, and evidence of transferability across development contexts. A coordinated pathway involving explainable-by-design models, standardised transferability testing, and regulatory learning environments is required to move pharmaceutical artificial intelligence from technical promise toward justified regulatory trust.
Pharmaceutical development is increasingly moving beyond the traditional emphasis on drug substance performance to include the full experience of medicine use. This shift reflects the recognition that therapeutic value is shaped not only by pharmacology, but also by whether patients can understand, accept, handle, administer, and continue using a product in everyday life. Despite major advances in dosage form engineering, digital health tools, adherence monitoring, and personalised pharmaceutical manufacturing, many innovations remain disconnected from the practical contexts in which medicines are used. A dosage form may be technically sophisticated but still fail if it is difficult to swallow, unattractive to children, burdensome for older adults, incompatible with daily routines, or unsupported by feedback systems that encourage continued use. This narrative review integrates three domains that are often discussed separately: dosage design, adherence logic, and real-world use systems. It argues that patient-centric pharmaceutical technologies should be understood as integrated use systems rather than isolated product features. The central question is how pharmaceutical technologies can be designed to support not only drug delivery, but also patient acceptance, behavioural continuity, and implementation in real healthcare settings. The review concludes that patient-centricity should be treated as a foundational development logic rather than a late-stage product attribute. Future pharmaceutical technologies will require early patient involvement, scalable manufacturing pathways, human factors validation, digital support systems, and regulatory strategies that define success according to real-world usability and patient-defined outcomes.
Eurasian Academy of Medicine and Dentistry