Study Of Viral Dynamics Research Articles

SARS-CoV-2 in digital era: Diagnostic techniques and importance of nucleic acid quantification with digital PCRs.

Studies related to clinical diagnosis and research of SARS-CoV-2 are important in the current pandemic era. Although molecular biology has emphasised the importance of qualitative analysis, quantitative analysis with nucleic acids in relation to SARS-CoV-2 needs to be clearly emphasised, which can provide perspective for viral dynamic studies of SARS-CoV-2. In this regard, the requirement and utilization of digital PCR in COVID-19 research has substantially increased during the pandemic, necessitating the aggregation of its cardinal applications and future scopes. Hence, this meta-review comprehensively addresses and emphasises the importance of nucleic acid quantification of SARS-CoV-2 RNA with digital PCR (dPCR). Various quantitative techniques of clinical significance like immunological, proteomic and nucleic acid-based diagnosis and quantification, have been comparatively discussed. Furthermore, the core part of the article focusses on the working principle and advantages of digital PCR, along with its applications in COVID-19 research. Several important applications like viral load quantitation, environmental surveillance and assay validation have been extensively investigated and discussed. Certain key future scopes of clinical importance, like mortality prediction, viral/variant-symbiosis, and antiviral studies were also identified, suggesting several possible digital PCR applications in COVID-19 research.

Multiparameter one‐sided tests for nonlinear mixed effects models with censored responses

Nonlinear mixed-effects (NLME) models are commonly used in longitudinal studies such as pharmacokinetics and HIV viral dynamics studies. NLME models are often derived based on underlying data-generating mechanisms, therefore the parameters in these models often have natural physical interpretations that may suggest reasonable constraints on certain parameters. For example, the HIV viral decay rates for populations receiving anti-HIV treatments may be reasonably expected to be nonnegative. Hypothesis testing for these parameters should incorporate practically reasonable constraints to increase statistical power. Motivated from HIV viral dynamic models, in this article we propose multiparameter one-sided or constrained tests for NLME models with censored responses, for example, viral dynamic models with viral loads subject to lower detection limits. We propose approximate likelihood-based tests that are computationally efficient. We evaluate the tests via simulations and show that the proposed tests are more powerful than the corresponding two-sided or unrestricted tests. We apply the proposed tests to two AIDS datasets with new findings.

Mathematical Modeling Applied to Understand the Dynamical Behavior of HIV Infection

The study of viral dynamics of HIV/AIDS has resulted in a deep understanding of host-pathogenesis of HIV infection from which numerous mathematical modeling have been derived. Most of these models are based on nonlinear ordinary differential equations. In Bangladesh, the rate of increase of HIV infection comparing with the other countries of the world is not so high. Bangladesh is still considered to be a low prevalent country in the region with prevalence and shown the local and global stability at disease free and chronic infected equilibrium points. Also we have shown that if the basic reproduction number , then HIV infection is cleared from T cell population and it converges to disease free equilibrium point. Whereas if , then HIV infection persists.

Automation Highlights from the Literature

Automation Highlights from the Literature

A hierarchical functional data analytic approach for analyzing physiologically based pharmacokinetic models

Ordinary differential equation (ODE) based models find application in a wide variety of biological and physiological phenomena. For instance, they arise in the description of gene regulatory networks, study of viral dynamics and other infectious diseases, etc. In the field of toxicology, they are used in physiologically based pharmacokinetic (PBPK) models for describing absorption, distribution, metabolism and excretion (ADME) of a chemical in-vivo. Knowledge about the model parameters is important for understanding the mechanism of action of a chemical and are often estimated using non-linear least squares methodology. However, there are several challenges associated with the usual methodology. Using functional data analytic methodology, in this article we develop a general framework for drawing inferences on parameters in models described by a system of differential equations. The proposed methodology takes into account variability between and within experimental units. The performance of the proposed methodology is evaluated using a simulation study and data obtained from a benzene inhalation study. We also describe a R-based software developed towards this purpose.

Equal HIV-1 Decay Kinetics in HSV-2–Infected and HSV-2–Uninfected Clinical Trial Participants Treated With Antiretroviral Therapy

Herpes simplex virus-2 increases HIV-1 viral load and may augment HIV-1 transmission probability. To test the hypothesis that lower HIV-1 clearance rates in HSV-2-infected persons may account for this higher HIV-1 viral load, we studied 149 participants from 3 ACTG viral dynamic studies (A315, A5160s, and A5166s). Though HIV-1 viral load was 0.19 logs higher in HSV-2-positive versus HSV-2-negative persons, first and second phase clearance rates during antiretroviral therapy were equal between participants in these 2 groups.

HIV DYNAMICS AND NATURAL HISTORY STUDIES: JOINT MODELING WITH DOUBLY INTERVAL-CENSORED EVENT TIME AND INFREQUENT LONGITUDINAL DATA.

Hepatitis C virus (HCV) coinfection has become one of the most challenging clinical situations to manage in HIV-infected patients. Recently the effect of HCV coinfection on HIV dynamics following initiation of highly active antiretroviral therapy (HAART) has drawn considerable attention. Post-HAART HIV dynamics are commonly studied in short-term clinical trials with frequent data collection design. For example, the elimination process of plasma virus during treatment is closely monitored with daily assessments in viral dynamics studies of AIDS clinical trials. In this article instead we use infrequent cohort data from long-term natural history studies and develop a model for characterizing post-HAART HIV dynamics and their associations with HCV coinfection. Specifically, we propose a joint model for doubly interval-censored data for the time between HAART initiation and viral suppression, and the longitudinal CD4 count measurements relative to the viral suppression. Inference is accomplished using a fully Bayesian approach. Doubly interval-censored data are modeled semiparametrically by Dirichlet process priors and Bayesian penalized splines are used for modeling population-level and individual-level mean CD4 count profiles. We use the proposed methods and data from the HIV Epidemiology Research Study (HERS) to investigate the effect of HCV coinfection on the response to HAART.

Modelling of viral dynamics in hepatitis B and hepatitis C clinical trials

In the recent years, studies of hepatitis B (HBV) and hepatitis C virus (HCV) dynamics have drawn great attention as they provide insight into the process of virus elimination/production and of infected cells decay during antiviral treatment. Estimates of viral dynamic parameters may be used to determine the lifetime of HCV/HBV virions and of infected cells, to estimate how long patients need to be treated and to evaluate antiviral therapies. The implementation of viral dynamics studies is difficult because they involve an intensive blood-sampling schedule and subsequent viral load quantification. In the majority of these studies, a model proposed by Neumann et al. (Science 1998; 282:103-107) is used under various assumptions, such as ignoring the delay in initial viral load decay, assuming time-constant treatment efficacy in reducing virion production and/or complete blocking of new infections, etc. However, only recently the effect of some of these assumptions on the estimated parameters has been evaluated. In this paper we provide a detailed review of the model, its underlying assumptions as well as the assumptions usually made by researchers during the design and analysis of viral dynamics studies. Then, we investigate the effect of these assumptions on the estimated parameters using simulations and draw useful conclusions concerning the analysis of these studies. Real data examples from a clinical trial on hepatitis B are provided as illustrations.

HIV‐1 Viral Dynamics Studies in the Setting of Clinical Trials—A Window of Opportunity

HIV‐1 Viral Dynamics Studies in the Setting of Clinical Trials—A Window of Opportunity

Statistical methods for HIV dynamic studies in AIDS clinical trials

Studies of HIV dynamics in AIDS research are very important for understanding pathogenesis of HIV infection and for assessing the potency of antiviral therapies. Since the viral dynamic results from clinical data were first published by Ho et al. and Wei et al., the study of HIV-1 dynamics in vivo has drawn a great attention from AIDS clinicians and researchers. Although the important findings from HIV dynamic studies have been published in many prestigious scientific journals, statistical methods for estimating viral dynamic parameters have not been paid enough attention by HIV dynamic investigators. The estimation methods in many viral dynamic studies are very crude and inefficient. In this paper, we review the statistical methods and mathematical models for HIV dynamic data analysis developed in recent years. We also address some practical issues and share our experiences in the design and analysis of viral dynamic studies. Some principles and guidelines for the design and analysis of viral dynamic studies are provided. The methodologies reviewed in this paper are also applicable to studies of other viruses such as hepatitis B virus or hepatitis C virus. We also pose some challenging statistical problems in this area in order to stimulate further study by the statistical research community.

Clinical relevance of viral dynamics and genotypes in hepatitis B virus.

Advances in molecular diagnostic technology make it possible to accurately measure viral loads and this has allowed the detailed study of viral dynamics of HIV, hepatitis C virus (HCV) and hepatitis B virus (HBV). Following antiviral therapy, there are at least two phases of viral load decay: one corresponding to clearance of free virions and a second, slower phase corresponding to eradication of infected cells. Application of mathematical models allows for the assessment of antiviral efficacy and improved design of therapeutic regimens. The clinical application of these tools should help optimize patient outcome. Another advantage of molecular diagnostics is characterization of the heterogeneity of viruses in particular patient populations under selective pressure situations. The HBV can be classified into seven major genotypes (A-G) that have mainly a geographic distribution. Recent genotypic studies have revealed the clinical and therapeutic relevance of viral genotyping in HBV infections.

Design of Viral Dynamic Studies for Efficiently Assessing Potency of Anti-HIV Therapies in AIDS Clinical Trials

The study of HIV dynamics is one of the most important developments in recent AIDS research. It greatly improves our understanding of the pathogenesis of HIV infection. Recently it has been proposed to use HIV dynamics to evaluate the efficacy of antiviral treatments. Currently a large number of AIDS clinical trials on HIV dynamics are in development worldwide. However, many design issues that arise from HIV dynamic studies have not been addressed. In this paper, we study these problems using intensive Monte Carlo simulations and analytic methods. We evaluate a finite number of feasible candidate designs, which are currently used and proposed in AIDS clinical trials from different perspectives. We compare the viral dynamic marker and classical viral load change markers in terms of power for identifying treatment difference, asymptotic relative efficiency, and sensitivity. Finally we propose some useful suggestions for practitioners based on our results.

Digital image analysis of the distribution of proliferating cell nuclear antigen in hepatitis C virus-related chronic hepatitis, cirrhosis, and hepatocellular carcinoma.

Viral dynamic studies in chronic hepatitis C virus (HCV) infection indicate a significantly shortened survival of virus-infected cells. Since at the steady state of chronic viral infection, the rate of infected cell elimination equals new cell regeneration, this would imply a high rate of hepatocyte turnover in chronic HCV liver disease. We estimated the fraction of regenerating hepatocytes in liver biopsy sections in chronic HCV liver disease, cirrhosis, and hepatocellular carcinoma (HCC). We used antibodies to proliferating cell nuclear antigen (PCNA) to detect proliferating cell nuclei in liver biopsy specimen from controls and patients with chronic hepatitis, cirrhosis, and HCC. We also used bis-benzimide to label fluorescently all hepatocyte nuclei simultaneously. Using digital image analysis, we calculated the area occupied by PCNA-stained hepatocyte nuclei, as a fraction of the total area occupied by fluorescently labeled hepatocyte nuclei (labeling index; LI). Antibody staining was negligible in the control specimen. The mean +/- SE PCNA LI increased from 0.21 +/- 0.1 in chronic hepatitis to 0.63 +/- 0.15 in HCC. There was no significant difference between chronic hepatitis and cirrhosis. The fraction of cells undergoing regeneration is increased in chronic HCV liver disease, HCV-related cirrhosis, and HCC. Increased hepatocyte turnover could provide the link between chronic HCV liver disease and HCC.

Pilot study of interferon-alpha high-dose induction therapy in combination with ribavirin plus amantadine for nonresponder patients with chronic hepatitis C.

Ribavirin plus interferon-alpha (IFN alpha) combination has led to a marked advance in the treatment of IFN alpha-naive or relapser patients with chronic hepatitis C but was shown to be only marginally effective in IFN alpha-nonresponders. We therefore conducted a pilot study to see whether an intensified treatment protocol might be more effective in inducing a virological response in patients who had not responded virologically to previous IFN alpha monotherapy. 14 nonresponder patients with histologically proven chronic hepatitis C were included in the study. Patients received 9 MU IFN alpha-2a daily for one week followed by 9 MU IFN alpha every second day for further 5 weeks. With the beginning of the seventh week, patients were treated with 6 MU IFN alpha thrice in week (tiw) for a period of 6 weeks (until week 12). IFN alpha was continued up to 48 weeks at a dose of 3 MU IFN alpha tiw. Ribavirin (1000-1200 mg/day) and amantadine sulphate (200 mg/day) was given orally for 48 weeks. One patient discontinued therapy after first IFN alpha injection and one other patient after 12 weeks of therapy because of side effects. The remaining 12 patients completed treatment according to the protocol. An initial virological response at week 24 was achieved in 2 of the 14 patients (14%) and both patients remained HCV RNA negative at the end of treatment. However, both patients relapsed 4 weeks after completion of therapy, and therefore none of the patients achieved a virological sustained response. Viral dynamics studies showed a marked decline in hepatitis C viremia during the first 6 weeks of high-dose IFN alpha. After IFN alpha dose reduction, however, viremia stabilized or increased in most patients. These data indicate, that even triple therapy with high-dose IFN alpha plus ribavirin and amantadine fails to improve significantly the response rates in IFN alpha-nonresponders.

Design of HIV viral dynamics studies

HIV viral dynamics studies involve repeated measurement of viral load in HIV-infected individuals, to asses short-term rates of viral load change in response to interventions such as initiation or withdrawal of antiviral therapy. Such studies are an important source of information on HIV pathogenesis. This paper concerns some statistical issues arising in their design. Using a linear random-effects model to incorporate between-patient differences in rates of viral load change, I discuss the choice of number of individuals and frequency of observation per individual. I suggest an approach for calculating the optimal sample size and observation frequency, based on minimizing the total number of viral load measurements that one needs to undertake. The conclusion, using this approach, is that over a period of linear change in viral load, three to five measurements per individual is generally appropriate. I also examine the observation frequency when the number of available individuals is limited, in which case it is shown that one can use a higher frequency of measurement per individual to achieve adequate power or precision. Finally, I consider sources of data for prior specification of variance components, together with conservative designs that are insensitive to a lack of prior information about between-patient differences. © 1998 John Wiley & Sons, Ltd.

Estimation of HIV dynamic parameters.

Investigation of HIV viral dynamics is important for understanding the HIV pathogenesis and for development of treatment strategies. Perelson et al. demonstrated that simple viral dynamic models fit to data on viral load as measured by plasma HIV-RNA could produce estimates of rates of clearance of virus and of infected CD4+ T-lymphocytes. In this paper we extend the work of Perelson et al. by proposing models with less restrictive assumptions about drug activity. Our models take into account the fact that infectious and non-infectious virions are produced by infected T-cells both before and after the treatment. We also show that direct measurement of infectious virus load provides sufficient information for estimation of antiretroviral drug efficacy parameter. For characterizing viral dynamics of populations and estimation of dynamic parameters, we propose a hierarchical non-linear model. Compared to other methods such as the non-linear least square method used by Perelson et al., we show that the proposed approach has the following advantages: (i) it is more appropriate for modelling within-patient and between-patient variation and to characterize the population dynamics; (ii) it is flexible enough to deal with both rich and sparse individual data; (iii) it has more power to detect model misspecification; (iv) it allows incorporation of covariates for viral dynamic parameters; (v) it makes more efficient use of between-subject information to get better parameter estimates. We give two simulation examples to illustrate the proposed approach and its advantages. Finally, we discuss practical issues regarding the clinical trial design for viral dynamic studies.

Design of HIV viral dynamics studies.

HIV viral dynamics studies involve repeated measurement of viral load in HIV-infected individuals, to assess short-term rates of viral load change in response to interventions such as initiation or withdrawal of antiviral therapy. Such studies are an important source of information on HIV pathogenesis. This paper concerns some statistical issues arising in their design. Using a linear random-effects model to incorporate between-patient differences in rates of viral load change, I discuss the choice of number of individuals and frequency of observation per individual. I suggest an approach for calculating the optimal sample size and observation frequency, based on minimizing the total number of viral load measurements that one needs to undertake. The conclusion, using this approach, is that over a period of linear change in viral load, three to five measurements per individual is generally appropriate. I also examine the observation frequency when the number of available individuals is limited, in which case it is shown that one can use a higher frequency of measurement per individual to achieve adequate power or precision. Finally, I consider sources of data for prior specification of variance components, together with conservative designs that are insensitive to a lack of prior information about between-patient differences.