A short update on the structure of drug binding sites on neurotransmitter transporters

<p>Abstract</p> <p>Background</p> <p>The dopamine (DAT), noradrenalin (NET) and serotonin (SERT) transporters are molecular targets for different classes of psychotropic drugs. Cocaine and the SSRI (<it>S</it>)-citalopram block neurotransmitter reuptake competitively, but while cocaine is a non-selective reuptake inhibitor, (<it>S</it>)-citalopram is a selective SERT inhibitor.</p> <p>Findings</p> <p>Here we present comparisons of the binding sites and the electrostatic potential surfaces (EPS) of DAT, NET and SERT homology models based on two different LeuT_Aatemplates; with a substrate (leucine) in an occluded conformation (PDB id <ext-link ext-link-id="2a65" ext-link-type="pdb">2a65</ext-link>), and with an inhibitor (tryptophan) in an open-to-out conformation (PDB id <ext-link ext-link-id="3f3a" ext-link-type="pdb">3f3a</ext-link>). In the occluded homology models, two conserved aromatic amino acids (tyrosine and phenylalanine) formed a gate between the putative binding pockets, and this contact was interrupted in the open to out conformation. The EPS of DAT and NET were generally negative in the vestibular area, whereas the EPS of the vestibular area of SERT was more neutral.</p> <p>Conclusions</p> <p>The findings presented here contribute as an update on the structure of the binding sites of DAT, NET and SERT. The updated models, which have larger ligand binding site areas than models based on other templates, may serve as improved tools for virtual ligand screening.</p

Incorporating Physiological and Biochemical Mechanisms into Pharmacokinetic-Pharmacodynamic Models: A Conceptual Framework

The aim of this conceptual framework paper is to contribute to the further development of the modelling of effects of drugs or toxic agents by an approach which is based on the underlying physiology and pathology of the biological processes. In general, modelling of data has the purpose (1) to describe experimental data, (2a) to reduce the amount of data resulting from an experiment, e.g. a clinical trial and (2b) to obtain the most relevant parameters, (3) to test hypotheses and (4) to make predictions within the boundaries of experimental conditions, e.g. range of doses tested (interpolation) and out of the boundaries of the experimental conditions, e.g. to extrapolate from animal data to the situation in man. Describing the drug/xenobiotic-target interaction and the chain of biological events following the interaction is the first step to build a biologically based model. This is an approach to represent the underlying biological mechanisms in qualitative and also quantitative terms thus being inherently connected in many aspects to systems biology. As the systems biology models may contain variables in the order of hundreds connected with differential equations, it is obvious that it is in most cases not possible to assign values to the variables resulting from experimental data. Reduction techniques may be used to create a manageable model which, however, captures the biologically meaningful events in qualitative and quantitative terms. Until now, some success has been obtained by applying empirical pharmacokinetic/pharmacodynamic models which describe direct and indirect relationships between the xenobiotic molecule and the effect, including tolerance. Some of the models may have physiological components built in the structure of the model and use parameter estimates from published data. In recent years, some progress toward semi-mechanistic models has been made, examples being chemotherapy-induced myelosuppression and glucose-endogenous insulin-antidiabetic drug interactions. We see a way forward by employing approaches to bridge the gap between systems biology and physiologically based kinetic and dynamic models. To be useful for decision making, the 'bridging' model should have a well founded mechanistic basis, but being reduced to the extent that its parameters can be deduced from experimental data, however capturing the biological/clinical essential details so that meaningful predictions and extrapolations can be made

Psychodynamic case formulations without technical language: a reliability study

Background To bridge the gap between symptoms and treatment, constructing case formulations is essential for clinicians. Limited scientific value has been attributed to case formulations because of problems with quality, reliability, and validity. For understanding, communication, and treatment planning beyond each specific clinician-patient dyad, a case formulation must convey valid information concerning the patient, as well as being a reliable source of information regardless of the clinician’s theoretical orientation. The first aim of the present study is to explore the completeness of unstructured psychodynamic formulations, according to four components outlined in the Case Formulation Content Coding Method (CFCCM). The second aim is to estimate the reliability of independent formulations and their components, using similarity ratings of matched versus mismatched cases. Methods This study explores psychodynamic case formulations as made by two or more experienced clinicians after listening to an evaluation interview. The clinicians structured the formulations freely, with the sole constraint that technical, theory-laden terminology should be avoided. The formulations were decomposed into components after all formulations had been written. Results The results indicated that most formulations were adequately comprehensive, and that overall reliability of the formulations was high (> 0.70) for both experienced and inexperienced clinician raters, although the lower bound reliability estimate of the formulation component deemed most difficult to rate - inferred mechanisms - was marginal, 0.61. Conclusions These results were achieved on case formulations made by experienced clinicians using simple experience-near language and minimizing technical concepts, which indicate a communicative quality in the formulations that make them clinically sound. Trial registration ClinicalTrials.gov Identifier: NCT00423462. https://doi.org/10.1007/s00432-018-2781-7 ., January 18, 2007