Daniel Scholes Rosenbloom

In vaccine efficacy trials, inaccurate counting of infection cases leads to systematic under-estimation—or “dilution”—of vaccine efficacy. In particular, if a sufficient fraction of observed cases are false positives, apparent efficacy will be greatly reduced, leading to unwarranted no-go decisions in vaccine development. Here, we propose a range of replicate testing strategies to address this problem, considering the additional challenge of uncertainty in both infection incidence and diagnostic assay specificity/sensitivity. A strategy that counts an infection case only if a majority of replicate assays return a positive result can substantially reduce efficacy dilution for assays with non-systematic (i.e., “random”) errors. We also find that a cost-effective variant of this strategy, using confirmatory assays only if an initial assay is positive, yields a comparable benefit. In clinical trials, where frequent longitudinal samples are needed to detect short-lived infections, this “confirmatory majority rule” strategy can prevent the accumulation of false positives from magnifying efficacy dilution. When widespread public health screening is used for viruses, such as SARS-CoV-2, that have non-differentiating features or may be asymptomatic, these strategies can also serve to reduce unneeded isolations caused by false positives.

Antiretroviral therapy inhibits HIV-1 replication but is not curative due to establishment of a persistent reservoir after virus integration into the host genome. Reservoir reduction is therefore an important HIV-1 cure strategy. Some HIV-1 nonnucleoside reverse transcriptase inhibitors induce HIV-1 selective cytotoxicity in vitro but require concentrations far exceeding approved dosages. Focusing on this secondary activity, we found bifunctional compounds with HIV-1–infected cell kill potency at clinically achievable concentrations. These targeted activator of cell kill (TACK) molecules bind the reverse transcriptase–p66 domain of monomeric Gag-Pol and act as allosteric modulators to accelerate dimerization, resulting in HIV-1 ⁺ cell death through premature intracellular viral protease activation. TACK molecules retain potent antiviral activity and selectively eliminate infected CD4 ⁺ T cells isolated from people living with HIV-1, supporting an immune-independent clearance strategy.

Background: Approximately 3.5 million children and adolescents worldwide are chronically infected with HCV. This study uses pharmacokinetic modeling to identify pediatric doses of elbasvir/grazoprevir (EBR/GZR) that achieve plasma concentrations similar to those seen in adults receiving the approved fixed-dose combination regimen of EBR/GZR. Patients and Methods: We conducted a nonrandomized, single-arm, multicenter, open-label phase 2b trial in children and adolescents aged 3 to <18 years with chronic HCV genotype 1 or 4 infection (NCT03379506). Pharmacokinetic data were used to bridge efficacy and safety data from adults to children in a stepwise (oldest to youngest) manner. A total of 57 participants were enrolled: cohort 1 (aged 12 to <18 y), n=22; cohort 2 (aged 7 to <12 y), n=17; and cohort 3 (aged 3 to <7 y), n=18. Results: Steady-state plasma exposures were achieved by week 4 for EBR and GZR in all cohorts and daily dosing achieved geometric mean steady-state area under the concentration-time curve at 0–24 hours that fell within comparability bounds established for adults. All participants achieved sustained virologic response 12 weeks after completing treatment (ie, undetectable HCV RNA 12 wk following completion of treatment). Headache (n=4), fatigue (n=4), and nausea (n=2) were the most common treatment-related adverse events (all mild or moderate); no participant discontinued because of an adverse event. Conclusions: Pediatric EBR/GZR pharmacokinetic models were successfully developed based on complex adult population pharmacokinetic models. At appropriate age-related doses, EBR/GZR is safe and effective in pediatric and adolescent participants with HCV infection.

Aim: Critical virus reagents in regulated bioanalytical assays require stability monitoring. Although stability at ultralow frozen temperatures is generally assumed, published data are limited and ...

Frequent viral load testing is necessary during analytical treatment interruptions (ATIs) in HIV cure-directed clinical trials, though such may be burdensome and inconvenient to trial participants. We implemented a national, cross-sectional survey in the United States to examine the acceptability of a novel home-based peripheral blood collection device for HIV viral load testing. Between June and August 2021, we distributed an online survey to people with HIV (PWH) and community members, biomedical HIV cure researchers and HIV care providers. We performed descriptive analyses to summarize the results. We received 73 survey responses, with 51 from community members, 12 from biomedical HIV cure researchers and 10 from HIV care providers. Of those, 51 (70%) were cisgender men and 50 (68%) reported living with HIV. Most (>80% overall) indicated that the device would be helpful during ATI trials and they would feel comfortable using it themselves or recommending it to their patients/participants. Of the 50 PWH, 42 (84%) indicated they would use the device if they were participating in an ATI trial and 27 (54%) also expressed a willingness to use the device outside of HIV cure studies. Increasing sensitivity of viral load tests and pluri-potency of the device (CD4 count, chemistries) would augment acceptability. Survey findings provide evidence that viral load home testing would be an important adjunct to ongoing HIV cure-directed trials involving ATIs. Survey findings may help inform successful implementation and uptake of the device in the context of personalized HIV care.

Quantitative viral outgrowth assays (QVOA) use limiting dilutions of CD4⁺ T cells to measure the size of the latent HIV-1 reservoir, a major obstacle to curing HIV-1. Efforts to reduce the reservoir require assays that can reliably quantify its size in blood and tissues. Although QVOA is regarded as a “gold standard” for reservoir measurement, little is known about its accuracy and precision or about how cell storage conditions or laboratory-specific practices affect results. Owing to this lack of knowledge, confidence intervals around reservoir size estimates—as well as judgments of the ability of therapeutic interventions to alter the size of the replication-competent but transcriptionally inactive latent reservoir—rely on theoretical statistical assumptions about dilution assays. To address this gap, we have carried out a Bayesian statistical analysis of QVOA reliability on 75 split samples of peripheral blood mononuclear cells (PBMC) from 5 antiretroviral therapy (ART)-suppressed participants, measured using four different QVOAs at separate labs, estimating assay precision and the effect of frozen cell storage on estimated reservoir size. We found that typical assay results are expected to differ from the true value by a factor of 1.6 to 1.9 up or down. Systematic assay differences comprised a 24-fold range between the assays with highest and lowest scales, likely reflecting differences in viral outgrowth readout and input cell stimulation protocols. We also found that controlled-rate freezing and storage of samples did not cause substantial differences in QVOA compared to use of fresh cells (95% probability of < 2-fold change), supporting continued use of frozen storage to allow transport and batched analysis of samples. Finally, we simulated an early-phase clinical trial to demonstrate that batched analysis of pre- and post-therapy samples may increase power to detect a three-fold reservoir reduction by 15 to 24 percentage points. The latent reservoir of resting CD4⁺ T cells is a major, if not the primary, obstacle to curing HIV. Quantitative viral outgrowth assays (QVOAs) are used to measure the latent reservoir in ART-suppressed HIV-infected people. Using QVOA is difficult, however, as the fraction of cells constituting the latent reservoir is typically about one in one million, far lower than other infectious disease biomarkers. To study reliability of these assays, we distributed 75 PBMC samples from five ART-suppressed HIV-infected participants among four labs, each conducting QVOA and following prespecified sample batching procedures. Using a Bayesian statistical method, we analyzed detailed assay output to understand how results varied within batches, between batches, and between labs. We found that, if batch variation can be controlled (i.e., a lab assays all samples in one batch), typical assay results are expected to differ from the true value by a factor of 1.6 to 1.9 up or down. We also found that freezing, storing, and thawing samples for later analysis caused no more than a 2-fold change in results. These outcomes, and the statistical methods developed to obtain them, should lead towards more precise and powerful assessments of HIV cure strategies.

Outcomes of anticancer therapy vary dramatically among patients due to diverse genetic and molecular backgrounds, highlighting extensive intertumoral heterogeneity. The fundamental tenet of precision oncology defines molecular characterization of tumors to guide optimal patient-tailored therapy. Towards this goal, we have established a compilation of pharmacological landscapes of 462 patient-derived tumor cells (PDCs) across 14 cancer types, together with genomic and transcriptomic profiling in 385 of these tumors. Compared with the traditional long-term cultured cancer cell line models, PDCs recapitulate the molecular properties and biology of the diseases more precisely. Here, we provide insights into dynamic pharmacogenomic associations, including molecular determinants that elicit therapeutic resistance to EGFR inhibitors, and the potential repurposing of ibrutinib (currently used in hematological malignancies) for EGFR-specific therapy in gliomas. Lastly, we present a potential implementation of PDC-derived drug sensitivities for the prediction of clinical response to targeted therapeutics using retrospective clinical studies.

Quantitative viral outgrowth assays (QVOA) use limiting dilutions of CD4+ T cells to measure the size of the latent HIV-1 reservoir, a major obstacle to curing HIV-1. Efforts to reduce the reservoir require assays that can reliably quantify its size in blood and tissues. Although QVOA is regarded as a “gold standard” for reservoir measurement, little is known about its accuracy and precision or about how cell storage conditions or laboratory-specific practices affect results. Owing to this lack of knowledge, confidence intervals around reservoir size estimates – as well as judgments of the ability of therapeutic interventions to alter the size of the replication-competent but transcriptionally inactive latent reservoir – rely on theoretical statistical assumptions about dilution assays. To address this gap, we have carried out a Bayesian statistical analysis of QVOA reliability on 75 split samples of peripheral blood mononuclear cells (PBMC) from 5 antiretroviral therapy (ART)-suppressed participants, measured using four different QVOAs at separate labs, estimating assay precision and the effect of frozen cell storage on estimated reservoir size. We found that typical assay results are expected to differ from the true value by a factor of 1.6 to 1.9 up or down. Systematic assay differences comprised a 24-fold range between the assays with highest and lowest scales, likely reflecting differences in viral outgrowth readout and input cell stimulation protocols. We also found that controlled-rate freezing and storage of samples did not cause substantial differences in QVOA compared to use of fresh cells (95% probability of < 2-fold change), supporting continued use of frozen storage to allow transport and batched analysis of samples. Finally, we simulated an early-phase clinical trial to demonstrate that batched analysis of pre- and post-therapy samples may increase power to detect a three-fold reservoir reduction by 15 to 24 percentage points. Author summary: The latent reservoir of resting CD4 ⁺ T cells is a major, if not the primary, obstacle to curing HIV. Quantitative viral outgrowth assays (QVOAs) are used to measure the latent reservoir in ART-suppressed HIV-infected people. Using QVOA is difficult, however, as the fraction of cells constituting the latent reservoir is typically about one in one million, far lower than other infectious disease biomarkers. To study reliability of these assays, we distributed 75 PBMC samples from five ART-suppressed HIV-infected participants among four labs, each conducting QVOA and following prespecified sample batching procedures. Using a Bayesian statistical method, we analyzed detailed assay output to understand how results varied within batches, between batches, and between labs. We found that, if batch variation can be controlled (i.e., a lab assays all samples in one batch), typical assay results are expected to differ from the true value by a factor of 1.6 to 1.9 up or down. We also found that freezing, storing, and thawing samples for later analysis caused no more than a 2-fold change in results. These outcomes, and the statistical methods developed to obtain them, should lead towards more precise and powerful assessments of HIV cure strategies.

Viral infections are one of the major causes of death worldwide, with HIV infection alone resulting in over 1.2 million casualties per year. Antiviral drugs are now being administered for a variety of viral infections, including HIV, hepatitis B and C, and influenza. These therapies target a specific phase of the virus’s life cycle, yet their ultimate success depends on a variety of factors, such as adherence to a prescribed regimen and the emergence of viral drug resistance. The epidemiology and evolution of drug resistance have been extensively characterized, and it is generally assumed that drug resistance arises from mutations that alter the virus’s susceptibility to the direct action of the drug. In this paper, we consider the possibility that a virus population can evolve towards synchronizing its life cycle with the pattern of drug therapy. The periodicity of the drug treatment could then allow for a virus strain whose life cycle length is a multiple of the dosing interval to replicate only when the concentration of the drug is lowest. This process, referred to as “drug tolerance by synchronization”, could allow the virus population to maximize its overall fitness without having to alter drug binding or complete its life cycle in the drug’s presence. We use mathematical models and stochastic simulations to show that life cycle synchronization can indeed be a mechanism of viral drug tolerance. We show that this effect is more likely to occur when the variability in both viral life cycle and drug dose timing are low. More generally, we find that in the presence of periodic drug levels, time-averaged calculations of viral fitness do not accurately predict drug levels needed to eradicate infection, even if there is no synchronization. We derive an analytical expression for viral fitness that is sufficient to explain the drug-pattern-dependent survival of strains with any life cycle length. We discuss the implications of these findings for clinically relevant antiviral strategies. Viral infections such as HIV, hepatitis B, hepatitis C, and influenza may be treated with antiviral drug therapy. These drugs generally block a specific phase of the virus’s life cycle, preventing it from replicating in the body. Often, a virus population can evolve drug resistance by acquiring mutations that prevent the drug from binding and inhibiting it. Here, we propose a new mechanism of drug resistance. We use mathematical models and stochastic simulations to show that virus populations can build resistance to antiviral therapies by synchronizing their life cycle with the dosing pattern of the drug. This process, which we refer to as “drug tolerance by synchronization”, can allow the virus to increase its overall fitness in the presence of the drug without altering drug binding. We show that viral strains whose life cycle lengths are approximately integer multiples of the time between drug doses possess an advantage during drug therapy. They can outcompete unsynchronized strains and lead to therapy failure with drugs that would otherwise have been successful.