Fadi Abikhalil

A new algorithm for computing the parameters of linear compartment models in pharmacokinetics

SummaryA new algorithm (FADHA) for computing pharmacokinetic parameter estimates has been developped.This technique is based on the simplex method which is used to minimize a nonlinear cost function. An important property of this program is that the convergence is ensured contrary to the well-known linear or nonlinear least-squares regression analysis which lead to a lack of convergence or to a false one. Two investigations of the comparative performances of FADHA program and other algorithms were undertaken (hexamethylmelamine and Piracetam ® pharmacokinetics). Least square analysis of data yielded biased estimates whereas FADHA estimates were unbiased and more precise. This new technique, takes into account all the possible observation errors and uses the concept of a weighting function rather than weights as such.

Pharmacokinetics and metabolism of hexamethylmelamine in mice after IP administration.

SummaryThe pharmacokinetics of hexamethylmelamine (HMM) and its first metabolite (hydroxymethylpentamethylmelamine HMPMM) following IP bolus dose of 200 mg/kg were studied in mice. The drug concentrations were determined by a sensitive reversed-phase HPLC assay. Thus, for the first time, HMM major hydroxylated and demethylated metabolite plasma levels canbedetermined at the same time. Pharmacokinetic data were analyzed by an original method using a nonlinear cost function minimized by a simplex algorithm. An important property of this computer program is that convergence is ensured in contrast to linear or nonlinear least-square regression analysis, which leads to lack of convergence or to false convergence. Both HMM and HMPMM data fit a one-compartment open model. The parameters obtained indicate that the parent drug would probably be rapidly and completely transformed by the human body into HMPMM.

Pharmacokinetics and metabolism of hexamethylmelamine in mice bearing renal cell tumors.

SummaryThe pharmacokinetics of hexamethylmelamine (HMM) and its main metabolites hydroxymethylpentamethylmelamine (HMPMM), pentamethylmelamine (PMM), and 2,2,4,6 tetramethylmelamine (2,2,4,6 TetrMM) were studied in renal cell (RC) tumor tissues and plasma of CDF1 mice that had received IP bolus injections of the maximally tolerated dose (200 mg/kg) of HMM. HMM, PMM, and 2,2,4,6 Tetr MM concentrations determined in RC tissues were much higher than the plasma values, as indicated by the pharmacokinetic parameters (Cmax and AUC). On the other hand, very low levels of HMPMM, generally considered to be a potentially active antitumor compound, were detected in the target tissues, whereas this hydroxylated metabolite was stable and easily determined in plasma. High HMM concentrations in RC tissues could correlate with the high sensitivity of the tumor to this drug. However, the behavior of HMPMM remains unclear; related hypotheses are presented in this paper.

Pharmacokinetics of daunorubicin and daunorubicinol in plasma, P388 and B16 tumours. Comparison with in vitro cytotoxicity data

SummaryThe comparison of pharmacokinetics of DNR in mouse plasma, in theDNR naturally resistant B16 melanoma and in theDNR naturally sensitive P388 leukemia showed that there is no direct correlation between total concentrations of this drug in tumours and the sensitivity resistance of these tissues.A finding which demonstrates the inadequacy of distribution models to select new potential anticancer drugs. Cytotoxicity of DNR and its metabolites to B16 melanoma and P388 leukemia cell lines were determined in vitro. Calculated inhibitory concentrations SO (IC50) were compared to maximal concentrations determined by pharmacokinetic studies.In all cases in vitro IC50 were lower than Cmax values. Moreover, resistant cells in vivo were found to be sensitive to DNR and metabolites when they are propagated in vitro.Tissue concentrations, as well as in vitro data, were fitted to appropriate models by an original program (FADHA) which uses the simplex method to minimize a non-linear cost function. Best fit models were chosen by statistical criteria.