Biomedical subjects

Michael T Klein

Publications and source records attributed to Michael T Klein.

3 recordsLinked to original sources

PubMed2006

Inhibition of highly productive HIV-1 infection in T cells, primary human macrophages, microglia, and astrocytes by Sargassum fusiforme.

BACKGROUND: The high rate of HIV-1 mutation and increasing resistance to currently available antiretroviral (ART) therapies highlight the need for new antiviral agents. Products derived from natural sources have been shown to inhibit HIV-1 replication during various stages of the virus life cycle, and therefore represent a potential source of novel therapeutic agents. To expand our arsenal of therapeutics against HIV-1 infection, we investigated aqueous extract from Sargassum fusiforme (S. fusiforme) for ability to inhibit HIV-1 infection in the periphery, in T cells and human macrophages, and for ability to inhibit in the central nervous system (CNS), in microglia and astrocytes. RESULTS: S. fusiforme extract blocked HIV-1 infection and replication by over 90% in T cells, human macrophages and microglia, and it also inhibited pseudotyped HIV-1 (VSV/NL4-3) infection in human astrocytes by over 70%. Inhibition was mediated against both CXCR4 (X4) and CCR5 (R5)-tropic HIV-1, was dose dependant and long lasting, did not inhibit cell growth or viability, was not toxic to cells, and was comparable to inhibition by the nucleoside analogue 2', 3'-didoxycytidine (ddC). S. fusiforme treatment blocked direct cell-to-cell infection spread. To investigate at which point of the virus life cycle this inhibition occurs, we infected T cells and CD4-negative primary human astrocytes with HIV-1 pseudotyped with envelope glycoprotein of vesicular stomatitis virus (VSV), which bypasses the HIV receptor requirements. Infection by pseudotyped HIV-1 (VSV/NL4-3) was also inhibited in a dose dependant manner, although up to 57% less, as compared to inhibition of native NL4-3, indicating post-entry interferences. CONCLUSION: This is the first report demonstrating S. fusiforme to be a potent inhibitor of highly productive HIV-1 infection and replication in T cells, in primary human macrophages, microglia, and astrocytes. Results with VSV/NL4-3 infection, suggest inhibition of both entry and post-entry events of the virus life cycle. Absence of cytotoxicity and high viability of treated cells also suggest that S. fusiforme is a potential source of novel naturally occurring antiretroviral compounds that inhibit HIV-1 infection and replication at more than one site of the virus life cycle.

Journal Article↗

PubMed2002

BioMOL: a computer-assisted biological modeling tool for complex chemical mixtures and biological processes at the molecular level.

A chemical engineering approach for the rigorous construction, solution, and optimization of detailed kinetic models for biological processes is described. This modeling capability addresses the required technical components of detailed kinetic modeling, namely, the modeling of reactant structure and composition, the building of the reaction network, the organization of model parameters, the solution of the kinetic model, and the optimization of the model. Even though this modeling approach has enjoyed successful application in the petroleum industry, its application to biomedical research has just begun. We propose to expand the horizons on classic pharmacokinetics and physiologically based pharmacokinetics (PBPK), where human or animal bodies were often described by a few compartments, by integrating PBPK with reaction network modeling described in this article. If one draws a parallel between an oil refinery, where the application of this modeling approach has been very successful, and a human body, the individual processing units in the oil refinery may be considered equivalent to the vital organs of the human body. Even though the cell or organ may be much more complicated, the complex biochemical reaction networks in each organ may be similarly modeled and linked in much the same way as the modeling of the entire oil refinery through linkage of the individual processing units. The integrated chemical engineering software package described in this article, BioMOL, denotes the biological application of molecular-oriented lumping. BioMOL can build a detailed model in 1-1,000 CPU sec using standard desktop hardware. The models solve and optimize using standard and widely available hardware and software and can be presented in the context of a user-friendly interface. We believe this is an engineering tool with great promise in its application to complex biological reaction networks.

Animals↗

PubMed2002

Application of biologically based computer modeling to simple or complex mixtures.

The complexity and the astronomic number of possible chemical mixtures preclude any systematic experimental assessment of toxicology of all potentially troublesome chemical mixtures. Thus, the use of computer modeling and mechanistic toxicology for the development of a predictive tool is a promising approach to deal with chemical mixtures. In the past 15 years or so, physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling has been applied to the toxicologic interactions of chemical mixtures. This approach is promising for relatively simple chemical mixtures; the most complicated chemical mixtures studied so far using this approach contained five or fewer component chemicals. In this presentation we provide some examples of the utility of PBPK/PD modeling for toxicologic interactions in chemical mixtures. The probability of developing predictive tools for simple mixtures using PBPK/PD modeling is high. Unfortunately, relatively few attempts have been made to develop paradigms to consider the risks posed by very complex chemical mixtures such as gasoline, diesel, tobacco smoke, etc. However, recent collaboration between scientists at Colorado State University and engineers at Rutgers University attempting to use reaction network modeling has created hope for the possible development of a modeling approach with the potential of predicting the outcome of toxicology of complex chemical mixtures. We discuss the applications of reaction network modeling in the context of petroleum refining and its potential for elucidating toxic interactions with mixtures.

Computer Simulation↗