Pre-clinical development of a combination microbicide vaginal ring containing dapivirine and darunavir.

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 IC50 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 10(4) 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 IC50 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.

HIV-1 genotyping of the protease-reverse transcriptase and integrase genes to detect mutations that confer antiretroviral resistance.

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.

Quantitative prediction of integrase inhibitor resistance from genotype through consensus linear regression modeling.

BACKGROUND: Integrase 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. METHODS: We 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. RESULTS: The 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. CONCLUSIONS: We 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.

Resistance to raltegravir highlights integrase mutations at codon 148 in conferring cross-resistance to a second-generation HIV-1 integrase inhibitor.

Raltegravir is the first integrase strand-transfer inhibitor (INSTI) approved for use in highly active antiretroviral therapy (HAART) for the management of HIV infection. Resistance to antiretrovirals can compromise the efficacy of HAART regimens. Therefore it is important to understand the emergence of resistance to RAL and cross-resistance to other INSTIs including potential second-generation INSTIs such as MK-2048. We have now studied the question of whether in vitro resistance selection (IVRS) with RAL initiated with viruses derived from clinical isolates would result in selection of resistance mutations consistent with those arising during treatment regimens with HAART containing RAL. Some correlation was observed between the primary mutations selected in vitro and during therapy, initiated with viruses with identical IN sequences. Additionally, phenotypic cross-resistance conferred by specific mutations to RAL and MK-2048 was quantified. N155H, a RAL-associated primary resistance mutation, was selected after IVRS with MK-2048, suggesting similar mechanisms of resistance to RAL and MK-2048. This was confirmed by phenotypic analysis of 766 clonal viruses harboring IN sequences isolated at the point of virological failure from 106 patients on HAART (including RAL), where mutation Q148H/K/R together with additional secondary mutations conferred reduced susceptibility to both RAL and MK-2048. A homology model of full length HIV-1 integrase complexed with viral DNA and RAL or MK-2048, based on an X-ray structure of the prototype foamy virus integrase-DNA complex, was used to explain resistance to RAL and cross-resistance to MK-2048. These findings will be important for the further discovery and profiling of next-generation INSTIs.