Marimastat was well absorbed from the gastrointestinal tract, with high levels of the study drug detected in plasma within hours after drug administration. Plasma concentrations of Marimastat achieved at dose levels 2 and 3 (50 mg and 100 mg orally twice daily) were substantially higher than those required for MMP inhibition in vitro. The dose-limiting toxicity (DLT) was severe inflammatory polyarthritis, which seemed to be a cumulative toxicity.

Quinidine causes greater QT prolongation in women than in men at equivalent serum concentrations. This difference may contribute to the greater incidence of drug-induced torsades de pointes observed in women taking quinidine and has implications for other cardiac and noncardiac drugs that prolong the QTc interval. Adjustment of dosages based on body size alone are unlikely to substantially reduce the increased risk of torsades de pointes in women.

Subject withdrawal from a study (also called dropout, or right censoring), is common in late phase clinical trials. A number of methods dealing with dropouts have been used in practice, the most common being "last observation carried forward" (LOCF). Many of these methods, including LOCF, can result in biased estimates of the efficacy or potency of the drug, especially in the modeling context. If the likelihood of dropout is correlated to the underlying unobserved data, the dropout is informative and should not be ignored in the modeling process. The topic of informative dropout in the context of longitudinal data has received much attention in the statistical literature, in the setting of linear and generalized linear models. We extend the approach to nonlinear models. The dropout hazard, as well as the longitudinal data, is modeled parametrically. Parameters are estimated by maximizing the approximate joint likelihood as implemented in the software NONMEM. Using data from actual clinical trials, we explore the impact of the dropout model on the ability of the joint model to predict observed longitudinal data patterns.

We describe a general and robust method for identification of an optimal non-linear mixed effects model. This includes structural, inter-individual random effects, covariate effects and residual error models using machine learning. This method is based on combinatorial optimization using genetic algorithm.

Degradation of basement membrane and extracellular matrix by matrix metalloproteinases (MMPs) is believed to be required for tumor invasion, tumor-induced angiogenesis and vascular invasion. A synthetic hydroxamate, batimastat (also known as BB-94), inhibits MMPs by binding the zinc ion in the active site of the MMP. Batimastat inhibits at least 50% of MMP activity at concentrations less than or equal to 10 ng/ml in vitro. Batimastat retarded ascites accumulation and increased survival in mice with human ovarian tumor xenografts. Acute and long-term toxicological studies revealed no major toxicity in animals. Batimastat is poorly soluble and was administered intraperitoneally (i.p.) as a suspension. Previous studies in patients with malignant ascites have shown no major toxicities at doses as high as 1350 mg/m2.

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Data from three pharmacokinetic drug interaction studies of amprenavir and ritonavir were used to develop a pharmacokinetic interaction model using NONMEM (nonlinear mixed-effect model). A two-compartment linear model with first-order absorption best fit the amprenavir data, while a one-compartment model was used to describe the ritonavir data. The inhibition of elimination of amprenavir by ritonavir was modeled with a maximum effect (E max ) inhibition model and the observed ritonavir concentration. Monte Carlo simulation was then used to predict amprenavir concentrations for various combinations of amprenavir and ritonavir in twice-daily and once-daily dosing regimens. Simulated minimum amprenavir concentrations in plasma (C min ) in twice-daily and once-daily dosing regimens were compared with protein binding-adjusted 50% inhibitory concentrations (IC 50 s) for clinical human immunodeficiency virus isolates with different susceptibilities to protease inhibitors (central tendency ratios). The model based on the first two studies predicted the results of the third study. Data from all three studies were then combined to refine the final model. The observed and simulated noncompartmental pharmacokinetic parameters agreed well. From this model, several candidate drug regimens were simulated. These simulations suggest that, in patients who have clinically failed a traditional amprenavir regimen, a regimen of 600 mg of amprenavir with 100 mg of ritonavir twice daily would result in C min -to-IC 50 ratios similar to that of 1,200 mg of amprenavir twice daily alone for wild-type viruses. In addition, once-daily regimens that result in C min s above the protein binding-corrected IC 50 s for wild-type virus are clearly feasible.Human immunodeficiency virus (HIV) protease inhibitors (PIs) in combination with reverse transcriptase inhibitors have prolonged and improved the quality of life for HIV-infected patients (2,9,10,17,21). Despite these advances, failure of therapeutic regimens to control viral replication is a major concern in the management of patients with HIV infection. Several factors are believed to contribute to this failure, including subtherapeutic drug concentrations resulting from drug interactions (22), adverse events (5), and poor adherence or nonadherence to the prescribed regimen because the regimens are complex and require frequent dosing with a large number of capsules (29).A strong correlation between antiviral activity and the concentrations of PIs in plasma has been demonstrated in several studies (1,7,8,18,26,28). Subtherapeutic concentrations of PIs in plasma can be associated with viral rebound and the development of drug resistance, indicating the importance of maintaining consistent drug concentrations in plasma (1,7,8,18,26,28). A regimen that results in increased trough exposures could potentially provide therapy for patients with drugresistant viruses and make once-daily (q.d.) dosing a feasible therapeutic option.Amprenavir (Agenerase; formerly 141W94) is a recently introduced HIV PI. ...

Drug development at different stages may require assessment of similarity of pharmacokinetics (PK). The common approach for such assessment when the difference is drug formulation is bioequivalence (BE), which employs a hypothesis test based on the evaluation of a 90% confidence interval for the ratio of average pharmacokinetic (PK) parameters. The role of formulation effect in BE assessment is replaced by subject population in PK similarity assessment. The traditional approach for BE requires that the PK parameters, primarily AUC and Cmax, be obtained from every individual. Unfortunately in many clinical circumstances, some or even all of the individuals may be sparsely sampled, making the individual evaluation difficult. In such cases, using models, particularly population models, becomes appealing. However, conducting an appropriate statistical test based on population modeling in a form consistent, at least in principle, with traditional 90% confidence interval approach is not so straightforward as it may appear. This manuscript proposes one such approach that can be applied to sparse sampling situations. The approach aims to maintain, as much as possible, the appropriateness of the hypothesis test. It is applied to data from clinical studies to address a need in drug development for assessment of PK similarity in different populations.