Hepatitis C viral evolution in genotype 1 treatment-naïve and treatment-experienced patients receiving telaprevir-based therapy in clinical trials

Background: In patients with genotype 1 chronic hepatitis C infection, telaprevir (TVR) in combination with peginterferon and ribavirin (PR) significantly increased sustained virologic response (SVR) rates compared with PR alone. However, genotypic changes could be observed in TVR-treated patients who did not achieve an SVR. Methods: Population sequence analysis of the NS3•4A region was performed in patients who did not achieve SVR with TVR-based treatment. Results: Resistant variants were observed after treatment with a telaprevir-based regimen in 12% of treatment-naïve patients (ADVANCE; T12PR arm), 6% of prior relapsers, 24% of prior partial responders, and 51% of prior null responder patients (REALIZE, T12PR48 arms). NS3 protease variants V36M, R155K, and V36M+R155K emerged frequently in patients with genotype 1a and V36A, T54A, and A156S/T in patients with genotype 1b. Lower-level resistance to telaprevir was conferred by V36A/M, T54A/S, R155K/T, and A156S variants; and higher-level resistance to telaprevir was conferred by A156T and V36M+R155K variants. Virologic failure during telaprevir treatment was more common in patients with genotype 1a and in prior PR nonresponder patients and was associated with higher-level telaprevir-resistant variants. Relapse was usually associated with wild-type or lower-level resistant variants. After treatment, viral populations were wild-type with a median time of 10 months for genotype 1a and 3 weeks for genotype 1b patients. Conclusions: A consistent, subtype-dependent resistance profile was observed in patients who did not achieve an SVR with telaprevir-based treatment. The primary role of TVR is to inhibit wild-type virus and variants with lower-levels of resistance to telaprevir. The complementary role of PR is to clear any remaining telaprevir-resistant variants, especially higher-level telaprevir-resistant variants. Resistant variants are detectable in most patients who fail to achieve SVR, but their levels decline over time after treatment

An inclusive Research and Education Community (iREC) model to facilitate undergraduate science education reform

Funding: This work was supported by Howard Hughes Medical Institute grants to DIH is GT12052 and MJG is GT15338.Over the last two decades, there have been numerous initiatives to improve undergraduate student outcomes in STEM. One model for scalable reform is the inclusive Research Education Community (iREC). In an iREC, STEM faculty from colleges and universities across the nation are supported to adopt and sustainably implement course-based research – a form of science pedagogy that enhances student learning and persistence in science. In this study, we used pathway modeling to develop a qualitative description that explicates the HHMI Science Education Alliance (SEA) iREC as a model for facilitating the successful adoption and continued advancement of new curricular content and pedagogy. In particular, outcomes that faculty realize through their participation in the SEA iREC were identified, organized by time, and functionally linked. The resulting pathway model was then revised and refined based on several rounds of feedback from over 100 faculty members in the SEA iREC who participated in the study. Our results show that in an iREC, STEM faculty organized as a long-standing community of practice leverage one another, outside expertise, and data to adopt, implement, and iteratively advance their pedagogy. The opportunity to collaborate in this manner and, additionally, to be recognized for pedagogical contributions sustainably engages STEM faculty in the advancement of their pedagogy. Here, we present a detailed pathway model of SEA that, together with underpinning features of an iREC identified in this study, offers a framework to facilitate transformations in undergraduate science education.Peer reviewe

Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease

BACKGROUND: Patients with atherosclerotic vascular disease remain at high risk for cardiovascular events despite effective statin-based treatment of low-density lipoprotein (LDL) cholesterol levels. The inhibition of cholesteryl ester transfer protein (CETP) by anacetrapib reduces LDL cholesterol levels and increases high-density lipoprotein (HDL) cholesterol levels. However, trials of other CETP inhibitors have shown neutral or adverse effects on cardiovascular outcomes. METHODS: We conducted a randomized, double-blind, placebo-controlled trial involving 30,449 adults with atherosclerotic vascular disease who were receiving intensive atorvastatin therapy and who had a mean LDL cholesterol level of 61 mg per deciliter (1.58 mmol per liter), a mean non-HDL cholesterol level of 92 mg per deciliter (2.38 mmol per liter), and a mean HDL cholesterol level of 40 mg per deciliter (1.03 mmol per liter). The patients were assigned to receive either 100 mg of anacetrapib once daily (15,225 patients) or matching placebo (15,224 patients). The primary outcome was the first major coronary event, a composite of coronary death, myocardial infarction, or coronary revascularization. RESULTS: During the median follow-up period of 4.1 years, the primary outcome occurred in significantly fewer patients in the anacetrapib group than in the placebo group (1640 of 15,225 patients [10.8%] vs. 1803 of 15,224 patients [11.8%]; rate ratio, 0.91; 95% confidence interval, 0.85 to 0.97; P=0.004). The relative difference in risk was similar across multiple prespecified subgroups. At the trial midpoint, the mean level of HDL cholesterol was higher by 43 mg per deciliter (1.12 mmol per liter) in the anacetrapib group than in the placebo group (a relative difference of 104%), and the mean level of non-HDL cholesterol was lower by 17 mg per deciliter (0.44 mmol per liter), a relative difference of -18%. There were no significant between-group differences in the risk of death, cancer, or other serious adverse events. CONCLUSIONS: Among patients with atherosclerotic vascular disease who were receiving intensive statin therapy, the use of anacetrapib resulted in a lower incidence of major coronary events than the use of placebo. (Funded by Merck and others; Current Controlled Trials number, ISRCTN48678192 ; ClinicalTrials.gov number, NCT01252953 ; and EudraCT number, 2010-023467-18 .)

Modeling viral evolutionary dynamics after telaprevir-based treatment.

For patients infected with hepatitis C virus (HCV), the combination of the direct-acting antiviral agent telaprevir, pegylated-interferon alfa (Peg-IFN), and ribavirin (RBV) significantly increases the chances of sustained virologic response (SVR) over treatment with Peg-IFN and RBV alone. If patients do not achieve SVR with telaprevir-based treatment, their viral population is often significantly enriched with telaprevir-resistant variants at the end of treatment. We sought to quantify the evolutionary dynamics of these post-treatment resistant variant populations. Previous estimates of these dynamics were limited by analyzing only population sequence data (20% sensitivity, qualitative resistance information) from 388 patients enrolled in Phase 3 clinical studies. Here we add clonal sequence analysis (5% sensitivity, quantitative) for a subset of these patients. We developed a computational model which integrates both the qualitative and quantitative sequence data, and which forms a framework for future analyses of drug resistance. The model was qualified by showing that deep-sequence data (1% sensitivity) from a subset of these patients are consistent with model predictions. When determining the median time for viral populations to revert to 20% resistance in these patients, the model predicts 8.3 (95% CI: 7.6, 8.4) months versus 10.7 (9.9, 12.8) months estimated using solely population sequence data for genotype 1a, and 1.0 (0.0, 1.4) months versus 0.9 (0.0, 2.7) months for genotype 1b. For each individual patient, the time to revert to 20% resistance predicted by the model was typically comparable to or faster than that estimated using solely population sequence data. Furthermore, the model predicts a median of 11.0 and 2.1 months after treatment failure for viral populations to revert to 99% wild-type in patients with HCV genotypes 1a or 1b, respectively. Our modeling approach provides a framework for projecting accurate, quantitative assessment of HCV resistance dynamics from a data set consisting of largely qualitative information

Population sequence-based and model-predicted median reversion times.

1<p>The 95% CI assumes a single value for each patient and does not incorporate uncertainity of individual predictions (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003772#pcbi.1003772.s005" target="_blank">Text S1</a>).</p

Kaplan-Meier curves for time-to-20% determined by population sequencing, model-predicted time-to-20%, and model-predicted time-to-1%.

<p>Results for patients with HCV subtypes 1a and 1b are shown in plots (A) and (B), respectively. Hash marks (∧) denote the censored observations indicating the time of the last visit for patients with virus that did not revert to <20% resistant. For clarity, these patients are explicitly denoted on the population sequence (“Pop. Seq.”) and model-predicted time-to-20% resistance curves only.</p

Hypothetical viral dynamics for a patient with viral breakthrough during telaprevir-based treatment.

<p>(A) Dynamics for the total viral load (Total, green), wild-type virus (WT, blue), and a telaprevir-resistant variant (Resistant, red) during and after treatment with telaprevir-based treatment. LOD is the limit of detection for the ‘total’ viral load quantification, and Seq. LOD is the limit of detection above which sequencing can be reliably performed (1000 IU/ml). The treatment phase is shown by the gray bar. (B) Corresponding percent resistance dynamics on a linear scale. Viral sequencing can be performed when the total viral load exceeds the sequencing assay LOD (solid red curve). The dashed lines at 20% and 5% show the limits of detection for population and clonal sequence data, respectively.</p

Comparison of the population sequence- and model-predicted time-to-20% for HCV subtypes 1a (A) and 1b (B).

<p>The X-axis represents the inferred time-to-loss of detectable resistance by population sequencing and reflects the first visit wherein the patient did not have detectable resistant variants. The Y-axis relies on the algorithms defined here, wherein the rate of loss is modeled continuously for each patient. The majority of the data points fall to the right of the unity line, indicating that the model predicts more rapid times-to-20% than those estimated from population sequence data.</p

Model fit for patients with genotype 1b HCV.

<p>(A): Histogram of the log₁₀ objective function values (φ; see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003772#pcbi.1003772.e021" target="_blank">Equation 5</a>) for all patients with genotype 1b. Dashed lines and numbers show quantile information for the fits. Also shown are representative fits for patients whose objective function values fall in the (B) 70%, (C) 90%, (D) 95%, and (E) 100% quantiles. Solid lines represent the model predictions, solid points represent the clonal sequence data, and error bars show the range for population sequence results.</p

Treatment Outcome in Patients from Phase 3 Telaprevir Studies.

<p>Data from ADVANCE includes only the T12PR arm and data from REALIZE includes pooled TVR arms. ‘Other’ includes patients with missing SVR assessment and patients with HCV RNA>25 IU/mL at last study dose but who did not have viral breakthrough. ‘Relapse’ here is calculated using a denominator of total number of patients, and so differs from a relapse rate calculated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034372#pone-0034372-g008" target="_blank">Figure 8</a> which uses patients with undetectable HCV RNA at the end of treatment. ‘SVR’ rates here are calculated as in the INCIVEK USPI, which utilized the last recorded HCV RNA assessment; in case of missing data, the last HCV RNA assessment from week 12 of follow-up onward was used. For the determination of SVR and relapse rates, the lower limit of quantification (<25 IU/ml) of the HCV RNA assay was used. These rates differ from SVR rates calculated according to the study protocol, which used the HCV RNA assessment at week 24 without carrying forward the prior HCV RNA data point in case of missing data, and the limit of detection (10–15 IU/ml) of the HCV RNA assay for SVR and relapse rate determination. SVR rates using the protocol analysis were: 75% for T12PR, 69% for T8PR and 44% for PR (ADVANCE, Jacobson 2011); 72%, 92% and 88% were recorded for the overall study (all patients), T12PR24 and T12PR48 randomized arms, respectively (ILLUMINATE, Sherman 2011); and 64%, 66% and 17% for T12PR48, lead-in T12PR48 and PR, respectively (REALIZE, Zeuzem 2011).</p