ABSTRACT
PURPOSE: Artificial intelligence (AI) refers to technology capable of mimicking human cognitive functions and has important applications across all sectors and industries, including drug development. This has considerable implications for the regulation of drug development processes, as it is expected to transform both the way drugs are brought to market and the systems through which this process is controlled. There is currently insufficient evidence in published literature of the real-world applications of AI. Therefore, this narrative review investigated, collated, and elucidated the applications of AI in drug development and its regulatory processes. METHODS: A narrative review was conducted to ascertain the role of AI in streamlining drug development and regulatory processes. FINDINGS: The findings of this review revealed that machine learning or deep learning, natural language processing, and robotic process automation were favored applications of AI. Each of them had considerable implications on the operations they were intended to support. Overall, the AI tools facilitated access and provided manageability of information for decision-making across the drug development lifecycle. However, the findings also indicate that additional work is required by regulatory authorities to set out appropriate guidance on applications of the technology, which has critical implications for safety, regulatory process workflow and product development costs. IMPLICATIONS: AI has adequately proven its utility in drug development, prompting further investigations into the translational value of its utility based on cost and time saved for the delivery of essential drugs.
ABSTRACT
Background: Limited evidence informs on the prevalence of chronic kidney disease (CKD) in people living with HIV (PLWH) in South Africa. Thus, this study aimed to determine the prevalence of CKD and its associated risk factors among PLWH within the rural province of Limpopo, South Africa. Methods: We conducted a cross-sectional study of 143 participants, subdivided into groups of PLWH (n = 103) and individuals without HIV (n = 43). Structured questionnaires were used to collect and capture sociodemographic information including age, sex, alcohol intake, smoking status, and educational status. Basic measurements taken included levels of cluster of differentiation 4 (CD4+) count, body mass index (BMI), blood pressure, plasma cystatin C, and fasting serum glucose levels. Plasma cystatin C-based estimated glomerular filtration rate (eGFR) was calculated using the chronic kidney disease epidemiology collaboration (CKD-EPI) estimator to determine the prevalence of CKD. Results: The prevalence of CKD was approximately 7% in PLWH. Multivariate logistic regression analysis showed that it was only diabetes mellitus (odds ratio of 5.795, 95% confidence interval, p = 0.034) and age (odds ratio of 1.078, 95% confidence interval, p = 0.039) that were significantly associated with CKD in PLWH. Conclusion: Chronic kidney disease was prevalent in PLWH, and it was further associated with cardiovascular risk factors, diabetes, and ageing. As PLWH age, the burden of CKD may be increased with the increase in cardiovascular-related comorbidities such as diabetes.