(Update) Dynamics of viral load rebound and immunological changes in two consecutive interruptions of anti-retroviral therapy
In Report 3 on the Resistance Workshop I discussed Felipe Garcia’s presentation at the Workshop on 10 individuals who interrupted therapy twice 6 months apart. We reported that Garcia said viral load rebounded after each interruption but following the rebound in the second interruption 4/9 had their viral load spontaneously decline and a proliferative p24 response was detected in 2 of these 4 only after the second stop. In an interview for the NATAP radio show, which will be aired Sunday August 1 at 11 PM on WOR Radio 710 AM in NYC and surrounding areas, Jose Gatell said the viral loads for these 4 people declined (0.8 to 2 log) but remained detectable. He said viral loads ranged from above 1,000 to about 40,000 copies/ml. Therapy was re-started and they all declined to undetectable. No resistance was detected. A third interruption will be conducted in about 6 months.

Scott Wegner, with the US Military HIV Research Program in Rockville MD, reported at the Resistance Workshop on this study of therapeutic drug monitoring. The authors used an assay to assess whether plasma levels remain above the minimum effective concentration throughout therapy for each individual drug. NVP, DLV, EFV, IDV, RTV, SQV, and NFV concentrations in human plasma (100ul) were determined by protein precipitation with acetonitrile followed by HPLC with MS/MS detection.

Scott Wegner presented an analysis of 150 random plasma samples along with drug regimen, time of administration of drugs, viral load, phenotypic and genotypic resistance information. A population pharmacokinetic (PK) model for 3 anti-HIV drugs (indinavir, nelfinavir and ritonavir) was built using measured plasma levels in 31, 41, and 10 patients, respectively.

Results-
In 10% (n=16) of the samples, particular components of the therapeutic regimen, mainly protease inhibitors and NNRTIs, were undetectable. In an additional 5% (n=7) of the samples, drug levels were below normal therapeutic values while in another 30% (n=44), drug levels were far above expected therapeutic values. Resistance profiles correlated well with therapy regimens and high viral loads, with the prevalence of samples with a VL of >1000 copies/ml being 20% higher in the group with low or undetectable levels of specific inhibitors.

Wegner said that a clear relationship exists between drug resistance, plasma concentration of the drug and clinical result taking viral load as a marker. In a number of patients with absence of drug resistance, too low plasma concentrations were the probable reason for therapy failure. He said – having serial measurements and clinical data, an estimate of trough concentrations could be obtained using a population PK model. Wegner concluded that therapeutic drug monitoring in combination with resistance testing and viral load determination, would probably be a strong tool in optimizing drug therapy.

Philippe Clevenbergh reported at the Resistance Workshop on the relevance of protease inhibitor plasma levels in patients guiding their treatment decisions by using genotypic resistance testing. He concluded that 30% of the 85 patients in this study, called the VIRADAPT Study, had sub-optimal protease inhibitor levels, and that having low trough levels was predictive of a reduced viral load response to therapy.

The stated goal of the investigators was to assess in a prospective randomization the relevance of plasma protease inhibitor trough levels in patients failing combination therapy managed with genotypic assay (HIV-RNA >10,000 copies/ml, at least 6 months NRTI treatment, and at least 3 months PI treatment).

The study authors said—in contrast to reverse transcriptase inhibitors, significant correlations between antiviral activity and plasma drug concentrations have been demonstrated for HIV protease inhibitors (references 1, 2, 3, 4) and that PI drug levels are significantly related to the decline in viral load (6, 7, 8, 9, 10). Patients were randomized in VIRADAPT to make treatment decisions based on standard of care, in consultation with doctor, and without the benefit of genotypic resistance testing, or to making treatment decision according to genotypic resistance mutations.

Analysis was on an intent-to-treat basis with viral load as the primary endpoint. Monthly PI plasma levels was performed in patients for 6 months. The levels of the PIs were determined by HPLC. Sub-optimal concentration (SOC) was defined as at least two PI trough plasma levels below a threshold defined as 2X IC95 (10). Patients were categorized into 4 groups: G1 (SOC/no genotype); G2 ( OC/no genotype); G3 (SOC/genotyping); G4 (OC/genotyping).

Results-
Eighty-five patients were included and were reported to be comparable in terms of risk factor, age, sex, previous treatment, CD4s, baseline HIV RNA, and mean plasma PI concentrations between the group using genotypic testing and the group without genotypic testing. Except for saquinavir. The saquinavir levels were higher in the group without genotypic testing. Statistical analysis showed a significant relationship between saquinavir concentration and decline of HIV-RNA in plasma. Patients who had OC (optimal concentration) showed a -1.28 log reduction in viral load at week 48 compared with –0.36 log in the SOC patients.

PI plasma trough concentration was an independent predictor of viral load reduction. The patients who had optimal drug levels and used genotypic testing to guide treatment decisions (G4) had the greatest log reduction in viral load. The patients without genotypic testing but with optimal drug concentration (G2) had the second greatest log reduction. Using statistical analysis the authors found the following factors were independent predictors of viral load response—drug concentration >IC95 X2 (O.R.=2.37, p=0.018), use of genotyping (O.R.=2.24, p=0.025), and the presence of primary PI mutations (O.R.=2.47, p=0.014).

The authors concluded that a minimum threshold of drug exposure may be required to provide optimum suppression of plasma viral load. I think trough concentrations that do not reach and maintain at certain levels may cause viral load failures. The authors suggest that therapeutic drug monitoring of PI plasma concentrations could optimize therapy and might be helpful in understanding treatment failure.

Genotypic and phenotypic data was available for 59 people who had failed at least 2 regimens and were started on MDRT. Their median viral load was 63,000 copies/ml and CD4s were 180 when MDRT was started. People were highly experienced: 3TC (98%), d4T (93%), AZT (90%), ddI (88%), indinavir (83%), saquinavir (76%), ritonavir (63%), ddC (53%), nevirapine (36%), nelfinavir (17%), delavirdine (9%), efavirenz (2%), and abacavir (2%). A median of 7 (2-12 range) drugs had been previously used. Phenotypic susceptibility was defined as a <10-fold increase in IC50 compared to wild-type using the Virco Antivirogram. Susceptibility by genotype was determined after sequence comparison with the Virco database.

Results-
Harrigan reported that there was a strong correlation between genotypic calls and phenotypic resistance for each drug except for abacavir and d4T (moderate correlation) and ddI (low correlation), perhaps because >10-fold resistance to these drugs was relatively rare in this study.

Initial response to therapy appeared to be related to the number of "effective" drugs taken: decreases in viral load after starting MDRT were 0, -0.9, -1.6, and –1.3 log for MDRT regimens including 0, 1, 2, and 3 effective drugs as measured by genotypic resistance and the Virco database. Or –0.2, -0.05, -0.9 or –1.5 log for 0, 1, 2, and 3 drugs measured by phenotype. It appears that susceptibility to 2-3 drugs by genotype or phenotype may be key. Harrigan reported that people with 5, 6, or 7 "effective" drugs in their regimen generally had a poorer response than those with only 1 or 2 "effective" drugs. There may have been some other unidentified reasons for this.

Multivariate analysis of predictors of response to abacavir: comparison of prior anti-retroviral therapy, baseline HIV RNA, CD4 count, and viral resistance
Randal Lanier, with Glaxo Wellcome, conducted a meta-analysis of 154 NRTI experienced individuals on the pooled data from 4 studies and reported the results at the Resistance Workshop. All 4 studies had the design of adding abacavir (ABC) to stable background. The objective of the meta-analysis was to explore baseline predictors of HIV RNA responses to ABC in individuals with NRTI experience who intensified their regimen with ABC. Plasma viral load was monitored with the Roche Amplicor Monitor (detection limit of 400 copies/ml). Reverse transcriptase genotypic changes were determined using population-based ABI sequencing. Resistance phenotypic testing was determined by Virco using the recombinant virus assay.

The majority of study participants had about 1 year prior therapy with 2 NRTIs and a low plasma viral load of about 5,000 copies/ml (3.7 log) with a range of 2.6-5.8 log. Seventy-nine percent of participants had prior experience with AZT, 74% with 3TC, 15% with ddI, 24% with d4T and 8% with ddC. Eighty-eight percent (135/154) entered study with NRTI mutations. One third (50/154) had 3 or more NRTI mutations.

Antiviral activity is measured in this study by a reduction to below 400 copies/ml or >0.5 log reduction. In brief, the data from the study suggests the 184 mutation alone does not reduce ABC antiviral activity. One or two AZT associated mutations may reduce the antiviral effect of ABC; 3 NRTI mutations will reduce the benefit significantly. Fifty percent of people in the study with 3 or more NRTI mutations received a reduction in viral load (>0.5 log reduction or <400 copies/ml). Although they only have phenotyping on 92 of 154 people in study no one with 8-fold or greater phenotypic resistance had a viral load response to abacavir.

The effect of Genotypic or Phenotypic Resistance to AZT with and without 3TC
Resistance; all samples have < 8-fold phenotypic resistance to abacavir at
baseline because authors find anyone with > 8 fold-phenotypic resistance to
abacavir don't respond to the drug--

Again, no one with 8 fold or more phenotypic resistance to abacavir responded.

Lanier also concluded that individuals with virus containing 3 or more NRTI mutations had a statistically significant (p=0.04) diminished likelihood of response to ABC.