Objectives: Combination microbicide vaginal rings may be more effective than single microbicide rings at reducing/preventing sexual transmission of HIV. Here, we report the pre-clinical development and macaque pharmacokinetics of matrix-type silicone elastomer vaginal rings containing dapivirine and darunavir.Methods: Macaque rings containing 25 mg dapivirine, 100 mg dapivirine, 300 mg darunavir or 100 mg dapivirine + 300 mg darunavir were manufactured and characterized by differential scanning calorimetry. In vitro release was assessed into isopropanol/water and simulated vaginal fluid. Macaque vaginal fluid and blood serum concentrations for both antiretrovirals were measured during 28 day ring use. Tissue levels were measured on day 28. Ex vivo challenge studies were performed on vaginal fluid samples and IC 50 values were calculated.Results: Darunavir caused a concentration-dependent reduction in the dapivirine melting temperature in both solid drug mixes and in the combination ring. In vitro release from rings was dependent on drug loading, the number of drugs present and the release medium. In macaques, serum concentrations of both microbicides were maintained between 10 1 and 10 2 pg/mL. Vaginal fluid levels ranged between 10 3 and 10 4 ng/g and between 104 and 10 5 ng/g for dapivirine and darunavir, respectively. Both dapivirine and darunavir showed very similar concentrations in each tissue type; the range of drug tissue concentrations followed the general rank order: vagina (1.8×10 3 -3.8×10 3 ng/g) .cervix (9.4×10 1 -3.9×10 2 ng/g) . uterus (0 -108 ng/g) .rectum (0 -40 ng/g). Measured IC 50 values were .2 ng/mL for both compounds.Conclusions: Based on these results, and in light of recent clinical progress of the 25 mg dapivirine ring, a combination vaginal ring containing dapivirine and darunavir is a viable second-generation HIV microbicide candidate.

The integrase inhibitor raltegravir (RAL) is currently used for the treatment of both treatment-naïve and treatment-experienced HIV-1-infected patients. Elvitegravir (EVG) is in late phases of clinical development. Since significant cross-resistance between RAL and EVG is observed, there is a need for second-generation integrase inhibitors (INIs) with a higher genetic barrier and limited cross-resistance to RAL/EVG. A panel of HIV-1 integrase recombinants, derived from plasma samples from raltegravir-treated patients (baseline and follow-up samples), were used to study the cross-resistance profile of two second-generation integrase inhibitors, MK-2048 and compound G. Samples with Q148H/R mutations had elevated fold change values with all compounds tested. Although samples with the Y143R/C mutation had reduced susceptibility to RAL, they remained susceptible to MK-2048 and compound G. Samples with the N155H mutation had no reduced susceptibility to compound G. In conclusion, our results allowed ranking of the INIs on the basis of the antiviral activities using recombinant virus stocks from RAL-treated patient viruses. The order according to decreasing susceptibility is compound G, MK-2048, and EVG.

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BackgroundIntegrase inhibitors (INI) form a new drug class in the treatment of HIV-1 patients. We developed a linear regression modeling approach to make a quantitative raltegravir (RAL) resistance phenotype prediction, as Fold Change in IC50 against a wild type virus, from mutations in the integrase genotype.MethodsWe developed a clonal genotype-phenotype database with 991 clones from 153 clinical isolates of INI naïve and RAL treated patients, and 28 site-directed mutants.We did the development of the RAL linear regression model in two stages, employing a genetic algorithm (GA) to select integrase mutations by consensus. First, we ran multiple GAs to generate first order linear regression models (GA models) that were stochastically optimized to reach a goal R2 accuracy, and consisted of a fixed-length subset of integrase mutations to estimate INI resistance. Secondly, we derived a consensus linear regression model in a forward stepwise regression procedure, considering integrase mutations or mutation pairs by descending prevalence in the GA models.ResultsThe most frequently occurring mutations in the GA models were 92Q, 97A, 143R and 155H (all 100%), 143G (90%), 148H/R (89%), 148K (88%), 151I (81%), 121Y (75%), 143C (72%), and 74M (69%). The RAL second order model contained 30 single mutations and five mutation pairs (p < 0.01): 143C/R&97A, 155H&97A/151I and 74M&151I. The R2 performance of this model on the clonal training data was 0.97, and 0.78 on an unseen population genotype-phenotype dataset of 171 clinical isolates from RAL treated and INI naïve patients.ConclusionsWe describe a systematic approach to derive a model for predicting INI resistance from a limited amount of clonal samples. Our RAL second order model is made available as an Additional file for calculating a resistance phenotype as the sum of integrase mutations and mutation pairs.

Major advances in antiretroviral (ARV) therapy during the last decade have made HIV-1 infections a chronic, manageable disease. In spite of these significant advancements, ARV drug resistance remains a hurdle for HIV-infected patients who are committed to lifelong treatments. Several commercially marketed and/or laboratory-developed tests (LDT) are available to detect resistance-associated mutations (RAMs) in HIV-1, by genotyping. These genotyping tests mainly comprise polymerase chain reaction (PCR)-amplification and population, nucleotide sequencing (Sanger methodology) of a large part of the protease (PR), reverse transcriptase (RT), and integrase (IN) genes. In this chapter, we describe HIV-1 PR, RT, and IN genotyping on clinical samples (plasma), using the LDT methodology performed at Janssen Diagnostics BVBA, Belgium (JDx), where the PR-RT genotyping is used as input, to generate a CE-marked vircoTYPE™ HIV-1 report while the IN genotyping is performed as a research-use-only (RUO) assay. The complete HIV-1 PR gene (297 bp; 99 amino acids) and a large part of the RT gene (the first 1,200 bp; 400 amino acids) are amplified and sequenced as a single 1,497 bp fragment. Genotyping of the IN gene is performed by amplification and sequencing of the RT-IN region (the last 459 bp; 153 amino acids of RT with the complete 867 bp; 289 amino acids of IN). This methodology allows identification of nucleoside/-nucleotide reverse transcriptase, non-nucleoside reverse transcriptase, protease, and integrase inhibitor (NRTI, NtRTI, NNRTI, PI, INI) RAMs in the PR-RT and IN genes, which allows to predict viral response against current ARV regimens.