Anti-apomorphine effects of phenothiazine drug metabolites

Classification of neuroleptic drugs - lack of relationship to effect on negative symptoms in schizophrenia

There is both pharmacological and clinical support for a classification of the schizophrenic syndrome into negative and positive subtypes. For neuroleptics that act upon both types of symptoms, it appears that lower doses are required for treatment of negative than for positive symptoms. Successful drug treatment of negative symptoms may therefore depend upon the choice of a correct dosage for the individual patient. Due to variation in pharamacokinetic parameters, similar doses of a neuroleptic drug may result in different plasma levels in individual patients, especially after oral medication. Pharmacokinetic variations, if not under proper control, may easily disguise a concentration-dependent relationship, such as the effects of a neuroleptic drug upon negative and positive symptoms. In drug treatment of negative symptoms it may therefore be an advantage to individualize the doses as a function of plasma drug level measurements, when available. No general relationship has been demonstrated between the chemical properties or pharmacodynamics of neuroleptic drugs and their potential to act upon negative symptoms. Also, the drugs which have been demonstrated to have an “energizing” effect have widely different pharmacokinetic properties. A chemical or pharmacological classification of neuroleptics therefore does not seem to give any information about their possible efficacy in treating negative symptoms in schizophrenia.

Modeling ADMET

Drug discovery and development is a costly and time-consuming endeavor (Calcoen et al. Nat Rev Drug Discov 14(3):161-162, 2015; The truly staggering cost of inventing new drugs. Forbes. http://www.forbes.com/sites/matthewherper/2012/02/10/the-truly-staggering-cost-of-inventing-new-drugs/, 2012; Scannell et al. Nat Rev Drug Discov 11(3):191-200, 2012). Over the last two decades, computational tools and in silico models to predict ADMET (Adsorption, Distribution, Metabolism, Excretion, and Toxicity) profiles of molecules have been incorporated into the drug discovery process mainly in an effort to avoid late-stage failures due to poor pharmacokinetics and toxicity. It is now widely recognized that ADMET issues should be addressed as early as possible in drug discovery. Here, we describe in detail how ADMET models can be developed and applied using a commercially available package, ADMET Predictor™ 7.2 (ADMET Predictor v7.2. Simulations Plus, Inc., Lancaster, CA, USA).

Tales from the war on error: the art and science of curating QSAR data

Curating the data underlying quantitative structure-activity relationship models is a never-ending struggle. Some curation can now be automated but much cannot, especially where data as complex as those pertaining to molecular absorption, distribution, metabolism, excretion, and toxicity are concerned (vide infra). The authors discuss some particularly challenging problem areas in terms of specific examples involving experimental context, incompleteness of data, confusion of units, problematic nomenclature, tautomerism, and misapplication of automated structure recognition tools.

Region specific distribution of levomepromazine in the human brain

OBJECTIVE

The aim of this study was to examine concentrations of levomepromazine and its metabolite desmethyl-levomepromazine in different regions of human brain and in relationship to drug-free time.

METHODS

Drug concentrations were measured in up to 43 regions of 5 postmortem human brains of patients previously treated with levomepromazine. To enable statistical comparison across brain regions several smaller brain areas were put together to form larger brain areas (cortex cerebri, limbic system, cerebellum, basal ganglia, thalamus). Mean values of drug concentrations in these larger brain areas were used in a repeated measurement ANOVA to analyze for region specific distribution. The elimination half-life in brain tissue was estimated with a NONMEM population kinetic analysis using the mean value of all brain regions of an individual case.

RESULTS

Levomepromazine and desmethyl-levomepromazine appear to accumulate in human brain tissue relative to blood. Mean concentrations differed largely between individual brains, in part due to differences in dose of drug, duration of treatment and drug-free time before death. There was an apparent region-specific difference in levomepromazine concentrations with highest values in the basal ganglia (mean 316 ng/g) and lowest values in the cortex cerebri (mean 209 ng/g). The elimination half-life from brain tissue is longer than from blood and was calculated to be about one week. Similar results were obtained with desmethyl-levomepromazine.

CONCLUSIONS

Levomepromazine shows a region-specific distribution in the human brain with highest values in the basal ganglia. This might be the consequence of low expression of the metabolic enzyme Cyp2D6 in the basal ganglia. If this finding is true also for other neuroleptic drugs it might increase our understanding of preferential toxicity of neuroleptic drugs against basal ganglia structures and higher volumes of basal ganglia of neuroleptic-treated patients. Furthermore, patients exposed to levomepromazine cannot be considered to be free of residual effects of the drug for a number of weeks after withdrawal.

Simultaneous determination of levomepromazine, midazolam and their major metabolites in human plasma by reversed-phase liquid chromatography

A sensitive and reliable high-performance liquid chromatographic (HPLC) assay is a prerequisite for pharmacokinetic analysis of continuous infusion of levomepromazine adjuvant to midazolam. We developed such a method to determine the levels of levomepromazine, midazolam and their major metabolites (levomepromazinesulfoxide, desmethyl-, didesmethyllevomepromazine, O-desmethyllevomepromazine and alpha-hydroxy-midazolam) simultaneously. Desmethylclomipramine was used as an internal standard (I.S.). The lower limit of quantification of this assay was set for levomepromazine 4.1 microg/l, levomepromazinesulfoxide 4.9 microg/l, O-desmethyllevomepromazine 18.4 microg/l, alpha-hydroxymidazolam 26.6 microg/l, midazolam 23.4 microg/l, didesmethyllevomepromazine 15.8 microg/l, and desmethyllevomepromazine 6.6 microg/l. The between- and within day assay variations were commonly below 5%. The recovery in human plasma for the different analytes varied between 85 and 11%. The accuracy of this assay varied between 95 and 105% for the different concentrations. The linearity of this assay was set between 25 and 800 microg/l (r(2)>0.999 of the regression line). The first results of pharmacokinetic analysis of midazolam indicated that half-life varied between 1.1 and 1.9 h. Pharmacokinetic analysis using a one-compartment model of levomepromazine revealed that the apparent volume of distribution was 4.1+/-2.4 l per kg lean body mass and the metabolic clearance was 309+/-225 l per hour per 70 kg. This assay proved to be robust and reproducible. It can reliably be used for further study of the pharmacokinetics of continuous infusion of levomepromazine.

Metabolism of levomepromazine in man

The phenothiazine drug, levomepromazine (LM), is used in the treatment of psychiatric disorders and as an analgesic. A single 50 or 100 mg dose of LM was given to healthy male volunteers, and urine samples were collected for 24 h. The urine was treated with beta-glucuronidase, purified by solid-phase extraction, and analyzed on a GC-MS system for identification of LM metabolites. Mass spectra suggesting 14 different LM metabolites were obtained from the samples. Our of these, 13 spectra could be ascribed to specific metabolites, 5 of which have not previously been identified. All these 5 metabolites were hydroxylated at the phenothiazine nucleus. Although the applied method did not determine the positions of hydroxyl groups on phenothiazine nuclei. 3 of the 5 metabolites were identified as O-desmethyl 3-hydroxy LM, O-desmethyl 7-hydroxy LM, and N,O-didesmethyl 7-hydroxy LM, based on their chromatographic properties. In addition two metabolites, one being hydroxylated on the phenothiazine nucleus, and one being O-demethylated and hydroxylated on the nucleus, were found. It is suggested that these were 8-hydroxy LM and O-desmethyl 8-hydroxy LM. The concentrations of 3-hydroxy LM (free+conjugated) appeared to be much higher than the concentrations of any other metabolite in the samples.

Effect of levomepromazine and metabolites on debrisoquine hydroxylation in the rat

The influence of the major metabolites of the phenothiazine derivative, levomepromazine (methotrimeprazine), on hydroxylation of debrisoquine was examined in male Sprague-Dawley rats. The metabolic ratio of debrisoquine/4-hydroxy debrisoquine was first determined in rats after oral administration of 10 mg/kg of debrisoquine. Then the same dose of debrisoquine was co-administered with various doses of levomepromazine or one of its metabolites. Levomepromazine and its sulphoxidated, N-demethylated and O-demethylated metabolites caused highly significant and dose-dependent increases in the debrisoquine metabolic ratio. 3-Hydroxy levomepromazine had no significant effect on the metabolism of debrisoquine. This indicates that the non-hydroxylated metabolites of levomepromazine have relatively high affinities for the cytochrome P450 enzyme which converts debrisoquine to 4-hydroxy debrisoquine in the rat. Such metabolites may therefore be responsible for a considerable part of the inhibitory effect of debrisoquine hydroxylation previously reported in patients treated with phenothiazine neuroleptics.

Structural changes by sulfoxidation of phenothiazine drugs

The side-chain conformations of psychoactive phenothiazine drugs in crystals are different from those of biologically inactive ring sulfoxide metabolites. This study examines the potential energies, molecular conformations and electrostatic potentials in chlorpromazine, levomepromazine (methotrimeprazine), their sulfoxide metabolites and methoxypromazine. The purpose of the study was to examine the significance of the different crystal conformations of active and inactive phenothiazine derivatives, and to determine why phenothiazine drugs lose most of their biological activity by sulfoxidation. Quantum mechanics and molecular mechanics calculations demonstrated that conformations with the side chain folded over the ring structure had lowest potential energy in vacuo, both in the drugs and in the sulfoxide metabolites. In the sulfoxides, side chain conformations corresponding to the crystal structure of chlorpromazine sulfoxide were characterized by stronger negative electrostatic potentials around the ring system than in the parent drugs. This may weaken the electrostatic interaction of sulfoxide metabolites with negatively charged domains in dopamine receptors, and cause the sulfoxides to be virtually inactive in dopamine receptor binding and related pharmacological tests.

Antagonism of presynaptic dopamine receptors by phenothiazine drug metabolites

Electrically evoked release of dopamine from the caudate nucleus is reduced by the dopamine receptor agonists, apomorphine and bromocriptine, and facilitated by neuroleptic drugs, which act as dopamine autoreceptor antagonists. The potencies of chlorpromazine, fluphenazine, levomepromazine and their hydroxy-metabolites in modulating electrically evoked release of dopamine were examined by superfusion of rabbit caudate nucleus slices pre-incubated with 3H-dopamine. O-Desmethyl levomepromazine, 3-hydroxy- and 7-hydroxy metabolites of chlorpromazine and levomepromazine facilitated electrically evoked release of 3H-dopamine, having potencies similar to that of the parent compounds. 7-Hydroxy fluphenazine was less active than fluphenazine in this system. These results indicate that phenolic metabolites of chlorpromazine and levomepromazine, but not of fluphenazine, may contribute to effects of the drugs mediated by presynaptic dopamine receptors.

Pharmacokinetics of neuroleptic drugs and the utility of plasma level monitoring

Variability in response to antipsychotic drug treatment may be caused by variable patient compliance, interactions with other drugs, pharmacokinetic variations and variations in concentration-response relationships at the receptor level. Pharmacokinetic variations may in some cases be compensated by individual dosage adjustments based on plasma drug level measurements. The interpatient variability in response to a certain time-course of drug concentrations at the receptor site could hitherto only be assessed by clinical judgement. New methods for in vivo assessment of receptor occupancy hold promise for possible measurement of parameters accounting for at least part of the interindividual variation in drug response at the receptor level. Monitoring of fluphenazine, perphenazine, thiothixene and sulpiride plasma levels by specific chemical assay methods seems to offer some guidance to individualization of drug doses. Definite therapeutic plasma level ranges have not been established for chlorpromazine and haloperidol. However, monitoring plasma levels of chlorpromazine or haloperidol might be of value when drug-induced toxicity is suspected, and as a means of controlling patient compliance.

Muscarinic cholinergic and histamine H1 receptor binding of phenothiazine drug metabolites

In vitro binding affinities of chlorpromazine, fluphenazine, levomepromazine, perphenazine and some of their metabolites for dopamine D2 receptors, alpha 1- and alpha 2 adrenoceptors in rat brain were previously reported from our laboratories. The present study reports the in vitro binding affinities of the same compounds for muscarinic cholinergic receptors and for histamine H1 receptors in rat brain, using 3H-quinuclidinyl benzilate and 3H-mepyramine as radioligands. Chlorpromazine, levomepromazine, and their metabolites had 5-30 times higher binding affinities for muscarinic cholinergic receptors than fluphenazine, perphenazine and their metabolites. Levomepromazine was the most potent and fluphenazine the least potent of the four drugs in histamine H1 receptor binding. 7-Hydroxy levomepromazine, 3-hydroxy levomepromazine and 7-hydroxy fluphenazine had only 10% of the potency of the parent drug in histamine H1 receptor binding, while the 7-hydroxy-metabolites of chlorpromazine and perphenazine had about 75% of the potency of the parent drug in this binding system. Their histamine H1 receptor binding affinities indicate that metabolites may contribute to the sedative effects of chlorpromazine and levomepromazine.