HIV cure: Are we going to make history?

BACKGROUND: At present, combination antiretroviral therapy (cART) is the mainstay for the treatment of people living with HIV/AIDS. cART can suppress the viral load to a minimal level; however, the possibility of the emergence of full-blown AIDS is always there. In the latter part of the first decade of the 21st century, an HIV-positive person received stem cell transplantation (SCT) for treatment of his haematological malignancy. The patient was able to achieve remission of the haematological condition as well as of HIV following SCT. Thorough investigations of various samples including blood and biopsy could not detect the virus in the person's body. The person was declared to be the first cured case of HIV. LITERATURE SEARCH: Over the next decade, a few more similar cases were observed and have recently been declared cured of the infection. A comprehensive search was performed in PubMed, Cochrane library and Google Scholar. Four such additional cases were found in literature. DESCRIPTION & DISCUSSION: These cases all share a common proposed mechanism for the HIV cure, that is, transplantation of stem cells from donors carrying a homozygous mutation in a gene encoding for CCR5 (receptor utilized by HIV for entry into the host cell), denoted as CCR5△32. This mutation makes the host immune cells devoid of CCR5, causing the host to acquire resistance against HIV. To the best of our knowledge, this is the first review to look at relevant and updated information of all cured cases of HIV as well as the related landmarks in history and discusses the underlying mechanism(s).

Risk of Congenital Anomalies with Dolutegravir-Based Anti-retroviral Regimens: A Systematic Review and Meta-analysis.

BACKGROUND AND OBJECTIVES: Dolutegravir has been used as a first-line anti-human immunodeficiency virus drug because of its better efficacy compared with other counterpart medicines. However, making a unanimous decision on its use during pregnancy has become difficult for stakeholders following congenital anomalies reported with its use. The objective of this systematic review and meta-analysis was to study the risk of congenital anomalies in newborns exposed to dolutegravir-based-regimens compared with those exposed to non-dolutegravir-based regimens during the antenatal period. METHODS: An extensive literature search was performed in MEDLINE (through PubMed), EMBASE, Cochrane Database of Systematic Reviews, Google Scholar, and ClinicalTrials.gov until 30 November, 2023. Studies reporting data on congenital anomalies following antenatal use of dolutegravir were included. Risk of bias for randomized controlled trials, non-randomized controlled trials, and observational studies was assessed using RoB2, ROBINS-I, and ROBINS-E tools, respectively. A meta-analysis was performed in 'RevMan 5.4.1' using a random-effects model. Heterogeneity was assessed by the 'Q' statistic and I2 value. A sensitivity analysis was performed for higher heterogeneity/high-risk studies. The study protocol was registered in PROSPERO [CRD42023446374] a priori. RESULTS: Of 26 eligible studies, 12 (six randomized controlled trials and six observational studies with a pooled sample of 32,617) were included in a meta-analysis and 14 in a qualitative synthesis only. The meta-analysis does not show a statistically significant difference in the risk of congenital anomalies between newborns exposed antenatally to dolutegravir-based regimen(s) and those exposed to non-dolutegravir-based regimens [risk ratio 1.10; 95% confidence interval 0.79-1.53; p = 0.59]. Heterogeneity was moderate (I2 = 47%). Pooled results for randomized controlled trials and observational studies separately and the sensitivity analysis for heterogeneity provided similar results. CONCLUSIONS: The risk of congenital anomalies was not significantly different between dolutegravir-based regimens and non-dolutegravir-based-regimens in newborns exposed during their antenatal period.

Artificial Intelligence and Machine Learning in Pharmacological Research: Bridging the Gap Between Data and Drug Discovery.

Artificial intelligence (AI) has transformed pharmacological research through machine learning, deep learning, and natural language processing. These advancements have greatly influenced drug discovery, development, and precision medicine. AI algorithms analyze vast biomedical data identifying potential drug targets, predicting efficacy, and optimizing lead compounds. AI has diverse applications in pharmacological research, including target identification, drug repurposing, virtual screening, de novo drug design, toxicity prediction, and personalized medicine. AI improves patient selection, trial design, and real-time data analysis in clinical trials, leading to enhanced safety and efficacy outcomes. Post-marketing surveillance utilizes AI-based systems to monitor adverse events, detect drug interactions, and support pharmacovigilance efforts. Machine learning models extract patterns from complex datasets, enabling accurate predictions and informed decision-making, thus accelerating drug discovery. Deep learning, specifically convolutional neural networks (CNN), excels in image analysis, aiding biomarker identification and optimizing drug formulation. Natural language processing facilitates the mining and analysis of scientific literature, unlocking valuable insights and information. However, the adoption of AI in pharmacological research raises ethical considerations. Ensuring data privacy and security, addressing algorithm bias and transparency, obtaining informed consent, and maintaining human oversight in decision-making are crucial ethical concerns. The responsible deployment of AI necessitates robust frameworks and regulations. The future of AI in pharmacological research is promising, with integration with emerging technologies like genomics, proteomics, and metabolomics offering the potential for personalized medicine and targeted therapies. Collaboration among academia, industry, and regulatory bodies is essential for the ethical implementation of AI in drug discovery and development. Continuous research and development in AI techniques and comprehensive training programs will empower scientists and healthcare professionals to fully exploit AI's potential, leading to improved patient outcomes and innovative pharmacological interventions.