Volume : 13, Issue : 08, August – 2026

Title:

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN PRECISION MEDICINE: APPLICATIONS IN MEDICAL BIOTECHNOLOGY

Authors :

Siddela Johnson*, Soma Sekhar Pulamarasetti, Kovvada Vandana, Bongu Ramya Priya, Varri Anudeepthi

Abstract :

Background: Artificial intelligence (AI) and machine learning (ML) are increasingly embedded across the precision medicine pipeline, from target identification to bedside decision support, promising more individualised diagnosis and treatment selection than population-averaged, one-size-fits-all approaches allow.
Objective: To critically synthesise current evidence on AI/ML applications in precision medicine within the domains of medical biotechnology, drug discovery, multi-omics and pharmacogenomics, medical imaging, clinical trial methodology, and clinical decision support, and to appraise the regulatory and ethical frameworks governing their translation into practice.
Data Sources: PubMed/MEDLINE, Scopus- and Web of Science-indexed journals, and publications from Nature Portfolio, The Lancet, Cell Press, Elsevier, Wiley, Oxford University Press, and MDPI were searched for literature published predominantly between 2019 and 2026, supplemented by landmark earlier studies and current regulatory guidance from the US Food and Drug Administration (FDA).
Review Methods: A narrative review format was adopted given the methodological heterogeneity and breadth of the subject area. Evidence was appraised for study design, data provenance, and generalisability, with emphasis on comparing findings across studies rather than sequential summarisation.
Key Findings: AI/ML tools now assist therapeutic target discovery and de novo molecular design, multi-omics-based patient stratification, radiomic and pathomic biomarker discovery, clinical trial risk prediction and patient-trial matching, and large language model (LLM)-based clinical decision support. Regulatory agencies have responded with lifecycle-oriented frameworks such as the FDA predetermined change control plan, yet prospective validation, external generalisability, and demonstrable impact on patient-relevant outcomes remain limited for most applications. Algorithmic bias arising from unrepresentative training data and proxy-outcome selection continues to threaten equitable deployment.
Conclusion: AI/ML methods offer credible, increasingly validated tools for advancing precision medicine within biotechnology and pharmaceutical sciences, but their clinical adoption should proceed in step with prospective evidence generation, total-product-lifecycle regulatory oversight, and deliberate bias mitigation.
Keywords
Precision Medicine; Artificial Intelligence; Machine Learning; Pharmacogenomics; Drug Discovery; Biotechnology, Medical

Cite This Article:

Please cite this article in press Battula Sammaiah et al., Artificial Intelligence And Machine Learning In Precision Medicine: Applications In Medical Biotechnology., Indo Am. J. P. Sci, 2026; 13(08).

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Volume : 13, Issue : 08, August – 2026

Title:

EVALUATION OF ANTIHYPERLIPIDEMIC ACTIVITY OF SAPINDUS EMARGINATUS IN RATS

Authors :

Chinthala Jamima*, Dr.R.narasimha Rao, Dr.N. Raghunandhan

Abstract :

Obesity and hyperlipidemia have become major disorders predominantly causing prevailing cardiovascular diseases and ultimately death. The prolonged use of anti-obesity drugs and statins for reducing obesity and blood lipid levels is leading toward adverse effects of kidneys and muscles, specifically rhabdomyolysis. The objective of this study is to evaluate potential of seeds of Sapindus emarginatus against hyperlipidemia. In this model of Hyperlipidemia, 30 adult male wistar rats (200-250gms) were evenly divided into 5 groups in both groups. Group-1 and Group-2 served as untreated and model controls respectively, while Group-3, 4 and 5 were the treatments groups which were simultaneously treated with standard, 100 and 200 mg/kg extract respectively along with High Fat Diet. On last day, blood samples for biochemical parameters, were obtained under inhaled diether anaesthesia. The outcomes of this study were expressed as mean standard error and data were evaluated by using analysis of variance followed by multiple comparisons. Oral administration of 100 mg/ kg and 200mg/kg body weight of Methanolic extract residual fraction of Moringa oleifera. Leaves exhibited a significant reduction (P < 0.01) in serum lipid parameters such as triglycerides, total cholesterol, low density lipoprotein (LDL), very LDL and increase in high density lipoprotein in hyperlipidemic rats when compared with hyperlipidemic control in both models. Our results demonstrated that Methanolic extract fraction of Sesbania grandiflora. Possessed significant antihyperlipidemic activity.
Keywords: Sesbania grandiflora, Cholesterol, LDL, triglycerides and antihyperlipidemic activity.

Cite This Article:

Please cite this article in press Chinthala Jamimaet al., Evaluation of Antihyperlipidemic activity of Sapindus Emarginatus in rats,, Indo Am. J. P. Sci, 2026; 13(08).

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