Inventum Biologicum: An International Journal of Biological Research

Artificial Intelligence in Precision Pharmacotherapy

Tue, 14 Jul 2026 00:00:00 +0000

Chronic disease is one of the major causes of death worldwide which generally not vaccine preventable are also don’t resolve spontaneously. The world health organization identifies four main types of chronic diseases including cardiovascular diseases (CVD, primarily heart disease and stroke), Cancer, chronic respiratory diseases and diabetes which are responsible for the majority of related deaths, while they can be treated, are seldom healed with medicine or other medical treatment. Chronic disease care is becoming increasingly confined, which frequently results in suboptimal therapeutic efficacy and adverse medication reactions. Pharmacotherapy involve in management and treatment of chronic diseases mainly associate with the dose and drug selection for the authorized treatment that significantly impact on outcomes. Artificial intelligence and machine learning are shifting pharmacotherapy from a reactive, standardize model to proactive personalized and data driven. Different AI driven software’s and technical platform along with their superiority in management of chronic diseases has elaborate in this review article. This article looks at the use of Artificial intelligence to optimize medicine selection and dosing for complicated, long-term illness. Apart from their promising outcomes, concern about mainly data privacy, model interpretability and also ethical regulatory barriers remain. This study explores the challenges emphasizing the significant importance of transparent, explainable AI in ensuring efficacy and safety in clinical practices. The use of AI in precision pharmacology has the potential to greatly improve therapeutic results, patient compliance and healthcare resource use for chronic conditions.

Stability Assessment of Pharmaceutical Products Under Accelerated Conditions

Sarvesh Tiwari, Subarna Kumar Mandal, Ashish Sarkar — Thu, 09 Jul 2026 00:00:00 +0000

An essential aspect of developing a pharmaceutical product is performing a stability assessment to ensure that the drug will be safe, effective, and perform as expected during its shelf life. To help predict long-term stability of pharmaceutical products, accelerated stability studies are frequently used by placing drug products under high temperature and humidity conditions for a predetermined period of time. The objective of the study was to evaluate the stability of pharmaceutical products stored under accelerated conditions in accordance with the International Council for Harmonisation (ICH) guidelines. Representative batches of the formulation were put into the appropriate container-closure system and stored at 40 ± 2 degrees Celsius and 75 ± 5 percent relative humidity (RH) for six months. Samples of the products were taken from the test conditions at pre-specified time points and analyzed for critical physical and chemical properties such as appearance, assay, dissolution, moisture content, pH, hardness, friability, and impurity profile. Based on the analysis results, all formulations remained within acceptable pharmacopoeial specifications for the duration of the study with minimal variations observed in physicochemical properties. The lack of significant degradation of the products or change in potency indicates sufficient stability at accelerated conditions. Accelerated stability studies yield useful information about predicted shelf-life, required storage conditions and suitability of packaging for pharmaceutical products. Furthermore, accelerated stability studies are important tools in providing quality assurance and regulatory compliance for developing stable and effective pharmaceutical formulations with predictable long-term performance.

Artificial Intelligence as a Catalyst for Structure Based Drug Design

Farooq Ahmad Mir — Wed, 08 Jul 2026 00:00:00 +0000

Drug discovery remains one of the most expensive and time-consuming endeavors in modern science, with the development of a single approved therapeutic typically requiring more than a decade of research and several billion dollars in investment. Over the past five years, artificial intelligence (AI) and machine learning (ML) have moved from peripheral computational aids to central drivers of the medicinal chemistry pipeline, reshaping how targets are identified, how hits are generated, and how lead compounds are optimized. This review synthesizes recent advances in AI-enabled drug discovery, with particular emphasis on structure-based and generative molecular design. We examine the principal categories of AI methodology in current use, including deep neural networks for bioactivity prediction, graph-based generative models for de novo molecule design, and transformer architectures for retrosynthesis planning. We further discuss landmark case studies demonstrating translational impact, most notably the discovery of the antibiotic halicin through deep learning-guided screening, and describe how structure-based generative frameworks are being benchmarked against traditional docking approaches. Finally, we critically evaluate persistent challenges relating to data quality, model interpretability, and regulatory acceptance, and outline directions likely to define the next decade of AI-augmented medicinal chemistry.

Predictors of Daytime Sleepiness Among University Students

Radhika Dixit, Priyank Singh, Ruby Parveen, Shahfaiz, Muskan Jain, Sonam Nidhi — Fri, 03 Jul 2026 00:00:00 +0000

Objective: To examine how sleep hygiene (SHI), demographic factors (age, gender), and sleep duration influence daytime sleepiness (ESS), and whether residential status (hosteler vs. day scholar) moderates these relationships.

Method: A cross-sectional, survey-based study was conducted among 240 college students aged 18–25 years from Moradabad, Uttar Pradesh. Participants were screened based on inclusion and exclusion criteria. Students with self-reported disturbed sleep patterns over the past two weeks were recruited. After obtaining written informed consent, a brief assessment was conducted. Outcome measures included the Epworth Sleepiness Scale (ESS) to evaluate daytime sleepiness and the Sleep Hygiene Index (SHI) to assess sleep-related behaviors. Data analysis was performed to examine the influence of sleep hygiene, demographics, and sleep duration on daytime sleepiness, and to explore the moderating effect of residential status (hosteler vs. day scholar).

Results: A total of 240 college students participated in the study. The mean age was 20.60 years (SD = 3.19), with some likely data entry errors noted. Participants reported an average sleep duration of 6.89 hours (SD = 1.82). The mean ESS score was 7.64 (SD = 4.31), indicating mild daytime sleepiness. The average SHI score was 20.25 (SD = 8.11), reflecting moderate variability and deficiencies in sleep hygiene. Overall, the data highlight suboptimal sleep habits and mild daytime sleepiness among the students.

Conclusion: The study concludes that poor sleep hygiene and shorter sleep duration are significant predictors of daytime sleepiness among university students. Additionally, residential status influences sleep behavior, with hostellers showing higher levels of sleep disruption. These findings underscore the need for targeted interventions to promote healthy sleep habits in this population.