CYP1A2 expression rather than genotype is associated with olanzapine concentration in psychiatric patients.

Olanzapine is a commonly prescribed atypical antipsychotic agent for treatment of patients with schizophrenia and bipolar disorders. Previous in vitro studies using human liver microsomes identified CYP1A2 and CYP2D6 enzymes being responsible for CYP-mediated metabolism of olanzapine. The present work focused on the impact of CYP1A2 and CYP2D6 genetic polymorphisms as well as of CYP1A2 metabolizing capacity influenced by non-genetic factors (sex, age, smoking) on olanzapine blood concentration in patients with psychiatric disorders (N = 139). CYP2D6 genotype-based phenotype appeared to have negligible contribution to olanzapine metabolism, whereas a dominant role of CYP1A2 in olanzapine exposure was confirmed. However, CYP1A2 expression rather than CYP1A2 genetic variability was demonstrated to be associated with olanzapine concentration in patients. Significant contribution of - 163C > A (rs762551), the most common SNP (single nucleotide polymorphism) in CYP1A2 gene, to enhanced inducibility was confirmed by an increase in CYP1A2 mRNA expression in smokers carrying - 163A, and smoking was found to have appreciable impact on olanzapine concentration normalized by the dose/bodyweight. Furthermore, patients' olanzapine exposure was in strong association with CYP1A2 expression; therefore, assaying CYP1A2 mRNA level in leukocytes can be an appropriate tool for the estimation of patients' olanzapine metabolizing capacity and may be relevant in optimizing olanzapine dosage.

Broadening the phenotype of the TWNK gene associated Perrault syndrome.

BACKGROUND: Perrault syndrome is a genetically heterogenous, very rare disease, characterized clinically by sensorineural hearing loss, ovarian dysfunction and neurological symptoms. We present the case of a 33 years old female patient with TWNK-associated Perrault syndrome. The TWNK gene is coding the mitochondrial protein Twinkle and currently there are only two reports characterizing the phenotype of TWNK-associated Perrault syndrome. None of these publications reported about special brain MRI alterations and neuropathological changes in the muscle and peripheral nerves. CASE PRESENTATION: Our patients with TWNK-dependent Perrault syndrome had severe bilateral hypoacusis, severe ataxia, polyneuropathy, lower limb spastic paraparesis with pyramidal signs, and gonadal dysgenesis. Psychiatric symptoms such as depression and paranoia were present as well. Brain MRI observed progressive cerebellar hyperintensive signs associated with cerebellar, medulla oblongata and cervical spinal cord atrophy. Light microscopy of the muscle biopsy detected severe neurogenic lesions. COX staining was centrally reduced in many muscle fibers. Both muscle and sural nerve electron microscopy detected slightly enlarged mitochondria with abnormal cristae surrounded by lipid vacuoles. In the sural nerve, dystrophic axons had focally uncompacted myelin lamellae present. Genetic investigation revealed multiple mtDNA deletion and compound heterozygous mutations of the TWNK gene (c.1196 A > G, c.1358 G > A). CONCLUSION: This study demonstrates that TWNK associated Perrault syndrome has a much broader phenotype as originally published. The coexistence of severe hypoacusis, spastic limb weakness, ataxia, polyneuropathy, gonadal dysgensia, hyperintense signals in the cerebellum and the presence of the mtDNA multiple deletion could indicate the impairment of the TWNK gene. This is the first report about pyramidal tract involvement and cerebellar MRI alteration associated with TWNK-related Perrault syndrome.

Drug effect prediction by polypharmacology-based interaction profiling.

Most drugs exert their effects via multitarget interactions, as hypothesized by polypharmacology. While these multitarget interactions are responsible for the clinical effect profiles of drugs, current methods have failed to uncover the complex relationships between them. Here, we introduce an approach which is able to relate complex drug-protein interaction profiles with effect profiles. Structural data and registered effect profiles of all small-molecule drugs were collected, and interactions to a series of nontarget protein binding sites of each drug were calculated. Statistical analyses confirmed a close relationship between the studied 177 major effect categories and interaction profiles of ca. 1200 FDA-approved small-molecule drugs. On the basis of this relationship, the effect profiles of drugs were revealed in their entirety, and hitherto uncovered effects could be predicted in a systematic manner. Our results show that the prediction power is independent of the composition of the protein set used for interaction profile generation.