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We aimed to develop a HIV risk scoring algorithm for targeted screening among women in South Africa. We used data from five biomedical intervention trials (N = 8982 Cox regression models were used to create a risk prediction algorithm and it was internally and externally validated using standard statistical measures; 7-factors were identified as significant predictors of HIV infection: <25 years old, being single/not cohabiting, parity (<3), age at sexual debut (<16), 3+ sexual partners, using injectables and diagnosis with a sexually transmitted infection(s). A score of ≥25 (out of 50) was the optimum cut point with 83% (80%) sensitivity in the development (validation) dataset. Our tool can be used in designing future HIV prevention research and guiding recruitment strategies as well as in health care settings. Identifying, targeting and prioritising women at highest risk will have significant impact on preventing new HIV infections by scaling up testing and pre-exposure prophylaxis in conjunction with other HIV prevention modalities.

We aimed to estimate the individual and joint impact of age, marital status and diagnosis with sexually transmitted infections (STIs) on HIV acquisition among young women at a population level in Durban, KwaZulu-Natal, South Africa. A total of 3,978 HIV seronegative women were recruited for four biomedical intervention trials from 2002–2009. Point and interval estimates of partial population attributable risk (PAR) were used to quantify the proportion of HIV seroconversions which can be prevented if a combination of risk factors is eliminated from a target population. More than 70% of the observed HIV acquisitions were collectively attributed to the three risk factors: younger age (<25 years old), unmarried and not cohabiting with a stable/regular partner and diagnosis with STIs. Addressing these risks requires targeted structural, behavioural, biomedical and cultural interventions in order to impact on unacceptably high HIV incidence rates among young women and the population as a whole.

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Using integrated in-silico computational techniques, including homology modeling, structure-based and pharmacophore-based virtual screening, molecular dynamic simulations, per-residue energy decomposition analysis and atom-based 3D-QSAR analysis, we proposed ten novel compounds as potential CCR5-dependent HIV-1 entry inhibitors. Via validated docking calculations, binding free energies revealed that novel leads demonstrated better binding affinities with CCR5 compared to maraviroc, an FDA-approved HIV-1 entry inhibitor and in clinical use. Per-residue interaction energy decomposition analysis on the averaged MD structure showed that hydrophobic active residues Trp86, Tyr89 and Tyr108 contributed the most to inhibitor binding. The validated 3D-QSAR model showed a high cross-validated r cv 2 value of 0.84 using three principal components and non-cross-validated r 2 value of 0.941. It was also revealed that almost all compounds in the test set and training set yielded a good predicted value. Information gained from this study could shed light on the activity of a new series of lead compounds as potential HIV entry inhibitors and serve as a powerful tool in the drug design and development machinery.