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Biomedical subjects

H Namini

Publications and source records attributed to H Namini.

3 recordsLinked to original sources

Safety and efficacy of adefovir dipivoxil in patients co-infected with HIV-1 and lamivudine-resistant hepatitis B virus: an open-label pilot study.

BACKGROUND: Lamivudine-resistant hepatitis B virus (HBV) is found in about 15-32% of infected patients with or without co-infection with HIV-1 after 1 year of lamivudine therapy. Adefovir dipivoxil is active in vivo and in vitro against wild-type and lamivudine-resistant HBV. We assessed the safety and efficacy of a once daily dose of adefovir dipivoxil in an open-label trial for the treatment of lamivudine-resistant HBV infection in HIV-1-infected patients. METHODS: 35 HIV-1/HBV co-infected patients receiving lamivudine therapy (150 mg twice daily) as part of their current HIV-1 antiretroviral regimen were enrolled. Patients received a 10 mg once-daily dose of adefovir dipivoxil for48 weeks while maintaining their existing anti-HIV-1 therapy, including lamivudine. Patients were assessed every 4 weeks for safety and efficacy. FINDINGS: Four patients withdrew from the study (two because of adverse events), leaving 31 patients who received adefovir dipivoxil for a median of 48 weeks (range 44-48). Mean decreases in serum HBV DNA concentrations from baseline (log 8.64 copies/mL [SE log 0.08]) were 2log 3.40 copies/mL [log 0.12] at week 24 (n=31) and 2log 4.01 copies/mL [log 0.17] at week 48 (n=29; p<0.0001). Two patients underwent hepatitis B e antigen seroconversion-one at week 32 and one at week 36. Adefovir dipivoxil was generally well tolerated, but was associated with a transient increase in serum alanine aminotransferase concentrations in 15 patients. We found no significant changes in either HIV-1 RNA or CD4 cell count. INTERPRETATION: These results indicate that 48 weeks of 10 mg daily adefovir dipivoxil is well tolerated and active against lamivudine-resistant HBV in HIV-1/HBV co-infected patients.

Adenine↗

Analysis of clinical data using neural nets.

Clinical studies investigate the interdependence of dosing regimen, efficacy, and side effects. These relationships often involve complex dynamical functions. The study of phenomena intermediate between dosing and efficacy, e.g., pharmacokinetics (PK), helps one identify and understand these interdependences. However, efficacy still tends to be a complicated function of PK parameters, and indeed these parameters are becoming more complex as a function of dosing regimen, viz., studies involving immunosuppressants, biotechnology drugs, sophisticated delivery systems, and dosing strategies. Stationary and time-dependent neural nets can help one identify and model such unknown complex dynamical functions with few assumptions and limited data (1-7). Neural nets can relate dosing directly to efficacy, dosing to PK, PK to efficacy, or any component in the complex associations among treatments, pharmacodynamics, efficacy, and side effects. Neural nets can also assist one in the design of clinical trials involving complex and sophisticated procedures, e.g., randomized controlled clinical trials.

Algorithms↗

Generalized ridge analysis with application to population pharmacokinetics/dynamics.

Given a set of measurements on a patient in clinical studies or on a drug response in bioassays and immunoassays, the data series for the individual can be incomplete, noisy, and haphazard. Hence, meaningful analysis on such limited data is at best difficult given the typical assumptions on the nonlinear structure of systematic and random components involving both individual and population effects. This paper describes a simple direct semiparametric procedure to incorporate population-wide information from as many individuals as required to support analysis of data on any specific individual. This notion is motivated by the pattern-processing capabilities of experts as they evaluate data on an individual based on their reservoir of accumulated knowledge of the given application on many individuals.

Biological Assay↗