Drug Resistance Mutations Among South African Children Living With HIV on WHO-recommended ART Regimens

Innovative strategies for the surveillance, prevention, and management of pediatric infections applied to low-income settings

INTRODUCTION

Infectious diseases still cause a significant burden of morbidity and mortality among children in low- and middle-income countries (LMICs). There are ample opportunities for innovation in surveillance, prevention, and management, with the ultimate goal of improving survival.

AREAS COVERED

This review discusses the current status in the use and development of innovative strategies for pediatric infectious diseases in LMICs by focusing on surveillance, diagnosis, prevention, and management. Topics covered are: Minimally Invasive Tissue Sampling as a technique to accurately ascertain the cause of death; Genetic Surveillance to trace the pathogen genomic diversity and emergence of resistance; Artificial Intelligence as a multidisciplinary tool; Portable noninvasive imaging methods; and Prognostic Biomarkers to triage and risk stratify pediatric patients.

EXPERT OPINION

To overcome the specific hurdles in child health for LMICs, some innovative strategies appear at the forefront of research. If the development of these next-generation tools remains focused on accessibility, sustainability and capacity building, reshaping epidemiological surveillance, diagnosis, and treatment in LMICs, can become a reality and result in a significant public health impact. Their integration with existing healthcare infrastructures may revolutionize disease detection and surveillance, and improve child health and survival.

Thirteen-year viral suppression and immunologic recovery of LPV/r-based regimens in pediatric HIV treatment: a multicenter cohort study in resource-constrained settings of China

Background

Antiretroviral Therapy (ART) in children remains challenging due to resource-constrained settings. We conducted a 13-year, prospective, multicenter cohort study on the effectiveness and safety of LPV/r-based regimens in ART-naive and ART-experienced children.

Methods

From January 2008 to May 2021, children living with HIV-1 were recruited with LPV/r-based regimens from 8 clinical research sites in 6 provinces in China. Effectiveness outcomes were virologic failure (defined as at least two consecutive measurements of VL > 200 copies/mL after 6 months of ART) and immune response (defined as CD4% recovered to more than 25% after 12 months of treatment). The safety outcomes were treatment-related grade 2-4 adverse events and abnormal laboratory test results.

Results

A total of 345 ART-naïve children and 113 ART-experienced children were included in this cohort study. The median follow-up time was 7.3 (IQR 5.5-10.5) years. The incidence density of virologic failure was 4.1 (95% CI 3.3-4.9) per 100 person-years in ART-naïve children and 5.0 (95% CI 3.5-6.5) per 100 person-years in ART-experienced children. Kaplan Meyer (KM) curve analysis showed children with ART experience were at a higher risk of virologic failure (p < 0.05). The risk factors of virologic failure in ART-naïve children were clinic setting in rural hospitals (aHR = 2.251, 1.108-4.575), annual missed dose times >5 days of LPV intake (aHR = 1.889, 1.004-3.554); The risk factor of virologic failure in ART-experienced children was missed dose times >5 days (aHR = 2.689, 1.299-5.604) and mother as caregivers for ART administration (aHR = 0.475, 0.238-0.948). However, during long-term treatment, viral suppression rates between ART-naïve and ART-experienced children remained similar. No significant differences were observed in the immune response, treatment-related grade 2-4 events, and abnormal laboratory test results between ART-naïve children and ART-experienced children.

Conclusion

Our research underscores that with consistent, long-term treatment of LPV/r-based regimens, ART-experienced children can achieve therapeutic outcomes comparable to ART-naïve children. It provides crucial insights on LPV/r-based regimens in pediatric HIV treatment, especially in resource-limited settings where high-cost Integrase Strand Transfer Inhibitors (INSTs) are inaccessible. This evidence-based understanding provides an essential addition to the global therapeutic strategies for pediatric HIV treatment.

HIV Drug Resistance Patterns and Characteristics Associated with Clinically Significant Drug Resistance among Children with Virologic Failure on Antiretroviral Treatment in Kenya: Findings from the Opt4Kids Randomized Controlled Trial

Increasing HIV drug resistance (DR) among children with HIV (CHIV) on antiretroviral treatment (ART) is concerning. CHIV ages 1-14 years enrolled from March 2019 to December 2020 from five facilities in Kisumu County, Kenya, were included. Children were randomized 1:1 to control (standard-of-care) or intervention (point-of-care viral load (POC VL) testing every three months with targeted genotypic drug resistance testing (DRT) for virologic failure (VF) (≥1000 copies/mL)). A multidisciplinary committee reviewed CHIV with DRT results and offered treatment recommendations. We describe DR mutations and present logistic regression models to identify factors associated with clinically significant DR. We enrolled 704 children in the study; the median age was 9 years (interquartile range (IQR) 7, 12), 344 (49%) were female, and the median time on ART was 5 years (IQR 3, 8). During the study period, 106 (15%) children had DRT results (84 intervention and 22 control). DRT detected mutations associated with DR in all participants tested, with 93 (88%) having major mutations, including 51 (54%) with dual-class resistance. A history of VF in the prior 2 years (adjusted odds ratio (aOR) 11.1; 95% confidence interval (CI) 6.3, 20.0) and less than 2 years on ART at enrollment (aOR 2.2; 95% CI 1.1, 4.4) were associated with increased odds of major DR. DR is highly prevalent among CHIV on ART with VF in Kenya. Factors associated with drug resistance may be used to determine which children should be prioritized for DRT.

Development and validation of a scoring system for the prediction of HIV drug resistance in Hubei province, China

Objective

The present study aimed to build and validate a new nomogram-based scoring system for the prediction of HIV drug resistance (HIVDR).

Design and methods

Totally 618 patients with HIV/AIDS were included. The predictive model was created using a retrospective set (N = 427) and internally validated with the remaining cases (N = 191). Multivariable logistic regression analysis was carried out to fit a model using candidate variables selected by Least absolute shrinkage and selection operator (LASSO) regression. The predictive model was first presented as a nomogram, then transformed into a simple and convenient scoring system and tested in the internal validation set.

Results

The developed scoring system consisted of age (2 points), duration of ART (5 points), treatment adherence (4 points), CD4 T cells (1 point) and HIV viral load (1 point). With a cutoff value of 7.5 points, the AUC, sensitivity, specificity, PLR and NLR values were 0.812, 82.13%, 64.55%, 2.32 and 0.28, respectively, in the training set. The novel scoring system exhibited a favorable diagnostic performance in both the training and validation sets.

Conclusion

The novel scoring system can be used for individualized prediction of HIVDR patients. It has satisfactory accuracy and good calibration, which is beneficial for clinical practice.

Bioinformatics toolbox for exploring target mutation-induced drug resistance

Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician's perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.