Evaluating the cost-effectiveness of a pre-exposure prophylaxis program for HIV prevention for men who have sex with men in Japan.

Men who have sex with men (MSM) have been disproportionally affected by the HIV epidemic in many countries, including Japan. Although pre-exposure prophylaxis (PrEP) is a strong prevention tool, it is not yet approved in Japan. A Markov model was developed to describe HIV infection and disease progression in an MSM cohort (N = 1000) in Japan receiving a PrEP program. The model was used to evaluate the cost-effectiveness of a PrEP program. HIV/AIDS treatment, screening, hospitalization due to AIDS, and PrEP were considered as costs and quality-adjusted life-years (QALYs) gained as utilities. Cost-effectiveness was assessed by comparing the incremental cost-effectiveness ratio (ICER) over a 30-year period against the willingness to pay (WTP) threshold. One-way sensitivity and probabilistic sensitivity analyses were performed. With 50% PrEP coverage, the PrEP program became dominant against the program without PrEP, using a threshold of 5.0 million JPY/QALY (45,455 USD). The probabilistic sensitivity analysis revealed that the PrEP program was dominant or at least cost-effective in most cases of 10,000 simulations. Therefore, preparing cheaper PrEP pills, which results in PrEP being dominant or ICER being lower than the WTP threshold, is important to make the program cost-effective. Introduction of PrEP to an MSM cohort in Japan would be cost-effective over a 30-year time horizon.

Progressive Cytopenia Developing during Treatment of Cryptococcosis in a Patient with HIV Infection and Bone Marrow Cryptococcal Infection.

Cytopenia is a common complication in patients with human immunodeficiency virus (HIV) infection. Identifying the cause is demanding because of the wide range of possible diagnoses. We herein report an HIV-infected patient with disseminated cryptococcosis involving multiple organs including the blood, brain, lungs, and bone marrow, who developed progressive pancytopenia after initiation of anti-fungal treatment with liposomal amphotericin-B (L-AMB) and flucytosine (5FC). The pancytopenia persisted despite early 5FC discontinuation. A bone marrow biopsy revealed cryptococcal infiltration and the blood examination findings recovered quickly after resuming L-AMB. Thus, this HIV-infected patient's pathological findings and clinical course suggested that the primary cause of the pancytopenia was bone marrow cryptococcosis.

Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study.

BACKGROUND: Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. METHODS AND FINDINGS: A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d-1 (95% CI: 1.06 to 1.27 d-1), 0.777 d-1 (0.716 to 0.838 d-1), and 0.450 d-1 (0.378 to 0.522 d-1) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. CONCLUSIONS: In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.

Potential anti-COVID-19 agents, cepharanthine and nelfinavir, and their usage for combination treatment.

Antiviral treatments targeting the coronavirus disease 2019 are urgently required. We screened a panel of already approved drugs in a cell culture model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and identified two new agents having higher antiviral potentials than the drug candidates such as remdesivir and chroloquine in VeroE6/TMPRSS2 cells: the anti-inflammatory drug cepharanthine and human immunodeficiency virus protease inhibitor nelfinavir. Cepharanthine inhibited SARS-CoV-2 entry through the blocking of viral binding to target cells, while nelfinavir suppressed viral replication partly by protease inhibition. Consistent with their different modes of action, synergistic effect of this combined treatment to limit SARS-CoV-2 proliferation was highlighted. Mathematical modeling in vitro antiviral activity coupled with the calculated total drug concentrations in the lung predicts that nelfinavir will shorten the period until viral clearance by 4.9 days and the combining cepharanthine/nelfinavir enhanced their predicted efficacy. These results warrant further evaluation of the potential anti-SARS-CoV-2 activity of cepharanthine and nelfinavir.

A quantitative model used to compare within-host SARS-CoV-2, MERS-CoV, and SARS-CoV dynamics provides insights into the pathogenesis and treatment of SARS-CoV-2.

The scientific community is focused on developing antiviral therapies to mitigate the impacts of the ongoing novel coronavirus disease 2019 (COVID-19) outbreak. This will be facilitated by improved understanding of viral dynamics within infected hosts. Here, using a mathematical model in combination with published viral load data, we compare within-host viral dynamics of SARS-CoV-2 with analogous dynamics of MERS-CoV and SARS-CoV. Our quantitative analyses using a mathematical model revealed that the within-host reproduction number at symptom onset of SARS-CoV-2 was statistically significantly larger than that of MERS-CoV and similar to that of SARS-CoV. In addition, the time from symptom onset to the viral load peak for SARS-CoV-2 infection was shorter than those of MERS-CoV and SARS-CoV. These findings suggest the difficulty of controlling SARS-CoV-2 infection by antivirals. We further used the viral dynamics model to predict the efficacy of potential antiviral drugs that have different modes of action. The efficacy was measured by the reduction in the viral load area under the curve (AUC). Our results indicate that therapies that block de novo infection or virus production are likely to be effective if and only if initiated before the viral load peak (which appears 2-3 days after symptom onset), but therapies that promote cytotoxicity of infected cells are likely to have effects with less sensitivity to the timing of treatment initiation. Furthermore, combining a therapy that promotes cytotoxicity and one that blocks de novo infection or virus production synergistically reduces the AUC with early treatment. Our unique modeling approach provides insights into the pathogenesis of SARS-CoV-2 and may be useful for development of antiviral therapies.

Estimation of the incubation period of COVID-19 using viral load data.

The incubation period, or the time from infection to symptom onset, of COVID-19 has usually been estimated by using data collected through interviews with cases and their contacts. However, this estimation is influenced by uncertainty in the cases' recall of exposure time. We propose a novel method that uses viral load data collected over time since hospitalization, hindcasting the timing of infection with a mathematical model for viral dynamics. As an example, we used reported data on viral load for 30 hospitalized patients from multiple countries (Singapore, China, Germany, and Korea) and estimated the incubation period. The median, 2.5, and 97.5 percentiles of the incubation period were 5.85 days (95 % CI: 5.05, 6.77), 2.65 days (2.04, 3.41), and 12.99 days (9.98, 16.79), respectively, which are comparable to the values estimated in previous studies. Using viral load to estimate the incubation period might be a useful approach, especially when it is impractical to directly observe the infection event.

HIV Testing by Public Health Centers and Municipalities and New HIV Cases During the COVID-19 Pandemic in Japan.

BACKGROUND: During the COVID-19 outbreak, facility capacity for HIV testing has been limited. Furthermore, people may have opted against HIV testing during this period to avoid COVID-19 exposure. We investigated the influence of the COVID-19 pandemic on HIV testing and the number of reported HIV cases in Japan. METHODS: We analyzed quarterly HIV/AIDS-related data from 2015 to the second quarter of 2020 using an anomaly detection approach. The data included the number of consultations, the number of HIV tests performed by public health centers or municipalities, and the number of newly reported HIV cases with and without an AIDS diagnosis. We further performed the same analysis for 2 subgroups: men who have sex with men (MSM) and non-Japanese persons. RESULTS: The number of HIV tests (9,584 vs. 35,908 in the year-before period) and consultations (11,689 vs. 32,565) performed by public health centers significantly declined in the second quarter of 2020, whereas the proportion of new HIV cases with an AIDS diagnosis (36.2% vs. 26.4%) significantly increased after removing the trend and seasonality effects. HIV cases without an AIDS diagnosis decreased (166 vs. 217), but the reduction was not significant. We confirmed similar trends for the men who have sex with men and non-Japanese subgroups. CONCLUSIONS: During the COVID-19 pandemic, the current HIV testing system in Japan seems to have missed more cases of HIV before developing AIDS. Continuously monitoring the situation and securing sufficient test resources by use of self-testing is essential to understand the clear epidemiological picture of HIV incidence during the COVID-19 pandemic.

Required concentration index quantifies effective drug combinations against hepatitis C virus infection.

Successful clinical drug development requires rational design of combination treatments based on preclinical data. Anti-hepatitis C virus (HCV) drugs exhibit significant diversity in antiviral effect. Dose-response assessments can be used to determine parameters profiling the diverse antiviral effect during combination treatment. In the current study, a combined experimental and mathematical approaches were used to compare and score different combinations of anti-HCV treatments. A "required concentration index" was generated and used to rank the antiviral profile of possible double- and triple-drug combinations against HCV genotype 1b and 2a. Rankings varied based on target HCV genotype. Interestingly, multidrug (double and triple) treatment not only augmented antiviral activity, but also reduced genotype-specific efficacy, suggesting another advantage of multidrug treatment. The current study provides a quantitative method for profiling drug combinations against viral genotypes, to better inform clinical drug development.

Quantifying the antiviral effect of APOBEC3 on HIV-1 infection in humanized mouse model.

APOBEC3 proteins inhibit human immunodeficiency virus (HIV)-1 infection by independently impairing viral reverse transcription and inducing G-to-A mutations in viral DNA. An HIV-1-encoded protein, viral infectivity factor (Vif), can counteract these antiviral activities of APOBEC3 proteins. Although previous studies using in vitro cell culture systems have revealed the molecular mechanisms of the antiviral action of APOBEC3 proteins and their antagonism by Vif, it remains unclear how APOBEC3 proteins affect the kinetics of HIV-1 replication in vivo. Here we quantified the time-series of viral load datasets from humanized mice infected with HIV-1 variants in the presence of APOBEC3F, APOBEC3G, or both APOBEC3F/G using a simple mathematical model that accounted for inter-individual variability. Through experimental and mathematical investigation, we formulated and calculated the total antiviral activity of APOBEC3F and APOBEC3G based on the estimated initial growth rates of viral loads in vivo. Interestingly, we quantitatively demonstrated that compared with APOBEC3G, the antiviral activity of APOBEC3F was widely distributed but skewed toward lower activity, although their mean values were similar. We concluded that APOBEC3G markedly and robustly restricted the initial stages of viral growth in vivo. This is the first report to quantitatively elucidate how APOBEC3F and APOBEC3G differ in their anti-HIV-1 modes in vivo.

Sporadic on/off switching of HTLV-1 Tax expression is crucial to maintain the whole population of virus-induced leukemic cells.

Viruses causing chronic infection artfully manipulate infected cells to enable viral persistence in vivo under the pressure of immunity. Human T-cell leukemia virus type 1 (HTLV-1) establishes persistent infection mainly in CD4+ T cells in vivo and induces leukemia in this subset. HTLV-1-encoded Tax is a critical transactivator of viral replication and a potent oncoprotein, but its significance in pathogenesis remains obscure due to its very low level of expression in vivo. Here, we show that Tax is expressed in a minor fraction of leukemic cells at any given time, and importantly, its expression spontaneously switches between on and off states. Live cell imaging revealed that the average duration of one episode of Tax expression is ∼19 hours. Knockdown of Tax rapidly induced apoptosis in most cells, indicating that Tax is critical for maintaining the population, even if its short-term expression is limited to a small subpopulation. Single-cell analysis and computational simulation suggest that transient Tax expression triggers antiapoptotic machinery, and this effect continues even after Tax expression is diminished; this activation of the antiapoptotic machinery is the critical event for maintaining the population. In addition, Tax is induced by various cytotoxic stresses and also promotes HTLV-1 replication. Thus, it seems that Tax protects infected cells from apoptosis and increases the chance of viral transmission at a critical moment. Keeping the expression of Tax minimal but inducible on demand is, therefore, a fundamental strategy of HTLV-1 to promote persistent infection and leukemogenesis.

Quantifying antiviral activity optimizes drug combinations against hepatitis C virus infection.

With the introduction of direct-acting antivirals (DAAs), treatment against hepatitis C virus (HCV) has significantly improved. To manage and control this worldwide infectious disease better, the "best" multidrug treatment is demanded based on scientific evidence. However, there is no method available that systematically quantifies and compares the antiviral efficacy and drug-resistance profiles of drug combinations. Based on experimental anti-HCV profiles in a cell culture system, we quantified the instantaneous inhibitory potential (IIP), which is the logarithm of the reduction in viral replication events, for both single drugs and multiple-drug combinations. From the calculated IIP of 15 anti-HCV drugs from different classes [telaprevir, danoprevir, asunaprevir, simeprevir, sofosbuvir (SOF), VX-222, dasabuvir, nesbuvir, tegobuvir, daclatasvir, ledipasvir, IFN-α, IFN-λ1, cyclosporin A, and SCY-635], we found that the nucleoside polymerase inhibitor SOF had one of the largest potentials to inhibit viral replication events. We also compared intrinsic antiviral activities of a panel of drug combinations. Our quantification analysis clearly indicated an advantage of triple-DAA treatments over double-DAA treatments, with triple-DAA treatments showing enhanced antiviral activity and a significantly lower probability for drug resistance to emerge at clinically relevant drug concentrations. Our framework provides quantitative information to consider in designing multidrug strategies before costly clinical trials.

Mathematical modeling of multi-drugs therapy: a challenge for determining the optimal combinations of antiviral drugs.

In the current era of antiviral drug therapy, combining multiple drugs is a primary approach for improving antiviral effects, reducing the doses of individual drugs, relieving the side effects of strong antiviral drugs, and preventing the emergence of drug-resistant viruses. Although a variety of new drugs have been developed for HIV, HCV and influenza virus, the optimal combinations of multiple drugs are incompletely understood. To optimize the benefits of multi-drugs combinations, we must investigate the interactions between the combined drugs and their target viruses. Mathematical models of viral infection dynamics provide an ideal tool for this purpose. Additionally, whether drug combinations computed by these models are synergistic can be assessed by two prominent drug combination theories, Loewe additivity and Bliss independence. By combining the mathematical modeling of virus dynamics with drug combination theories, we could show the principles by which drug combinations yield a synergistic effect. Here, we describe the theoretical aspects of multi-drugs therapy and discuss their application to antiviral research.

Quantification of deaminase activity-dependent and -independent restriction of HIV-1 replication mediated by APOBEC3F and APOBEC3G through experimental-mathematical investigation.

APOBEC3F and APOBEC3G cytidine deaminases potently inhibit human immunodeficiency virus type 1 (HIV-1) replication by enzymatically inserting G-to-A mutations in viral DNA and/or impairing viral reverse transcription independently of their deaminase activity. Through experimental and mathematical investigation, here we quantitatively demonstrate that 99.3% of the antiviral effect of APOBEC3G is dependent on its deaminase activity, whereas 30.2% of the antiviral effect of APOBEC3F is attributed to deaminase-independent ability. This is the first report quantitatively elucidating how APOBEC3F and APOBEC3G differ in their anti-HIV-1 modes.

Quantification of viral infection dynamics in animal experiments.

Analyzing the time-course of several viral infections using mathematical models based on experimental data can provide important quantitative insights regarding infection dynamics. Over the past decade, the importance and significance of mathematical modeling has been gaining recognition among virologists. In the near future, many animal models of human-specific infections and experimental data from high-throughput techniques will become available. This will provide us with the opportunity to develop new quantitative approaches, combining experimental and mathematical analyses. In this paper, we review the various quantitative analyses of viral infections and discuss their possible applications.

Mathematical study of the role of Delta/Notch lateral inhibition during primary branching of Drosophila trachea development.

A wide range of cellular developmental processes employ intercellular signaling via the Delta/Notch lateral inhibitory pathway to achieve stable spatial patterning. Recent genetic experiments have shown the importance of Delta/Notch lateral inhibition for regulating the number of tip cells in the tracheal primary branching of Drosophila. To examine the role of Delta/Notch regulation in the tip-cell selection, we analyzed a mathematical model of a simple lateral inhibitory system having input signals. Mathematical and numerical analyses revealed that the lateral inhibition did not amplify the signal difference between neighboring cells over the parameter ranges in which the spatial pattern of tip selection was realized. We also show that the number of tip cells becomes less affected by a fluctuation of the input gradient signal as the lateral inhibition becomes stronger. In addition, we demonstrate that the lateral inhibitory regulation enhances the robustness of the tip-cell selection compared with a system regulated by self-inhibition, an alternative means of inhibitory regulation. These results suggest that the lateral inhibition promotes the robustness of tip-cell selection in the tracheal development of Drosophila.