Difficulties in the differential diagnosis of proteinuria during pregnancy / A várandósság során jelentkezo proteinuria differenciáldiagnosztikájának nehézségei

Even during normal pregnancy, significant morphological, functional and hemodynamic changes take place in the kidneys, resulting in a slightly increased proteinuria. However, an abnormal increase, especially if accompanied by hypertension or impaired renal function, requires close maternal and fetal follow-up, as it may predict severe perina-tal complications. Differential diagnosis of proteinuria is diverse, and the primary consideration in clarifying the etiol-ogy is to differentiate between preeclampsia and other possible primary kidney disease. We list all the diseases on the etiological palette that may even mimic the symptoms of preeclampsia, making it difficult to make an accurate diag-nosis. In the case of a 31-year-old gravida, we review the differential diagnosis of progressive proteinuria observed during pregnancy. In addition to the diagnosis of postpartum preeclampsia, renal malignancy was confirmed. We are also looking for the answer whether malignant kidney cancer can be blamed for the clinical presentation that includes hypertension, progressive proteinuria.

Enhanced hit-to-lead process using bioanalogous lead evolution and chemogenomics: application in designing selective matrix metalloprotease inhibitors.

The authors describe an innovative approach for designing novel inhibitors. This approach effectively integrates the emerging chemogenomics concept of target-family-based drug discovery with bioanalogous design strategies, including privileged structures, molecular frameworks as well as bioisosteric and bioanalogous/isofunctional modifications. The authors applied this method in the design of selective inhibitors of matrix metalloproteases (MMPs), also referred to as matrixins, on the basis of a unique analysis of the ligand-target knowledge base, the 'matrixinome'. For this analysis, the authors created an annotated MMP database containing ∼ 300 inhibitors with their published activity profile. The ligand space was then arranged into a lead evolution tree, where the substructural transformations in each virtual step led to marked changes in the activity pattern. This allowed subtype-specific privileged fragments to be extracted as well as modifications, which improve activity and/or selectivity. Furthermore, the compounds with the preferred activity profile were correlated with sequence homology as well as binding site similarity within the target family, thereby leading to the identification of substructural modifications that turn non-selective, biohomologous structures into selective inhibitors. The matrixinomic application of the authors' approach, therefore, provides an example of how the combination of ligand space knowledge with sequence-related data can radically improve the outcome of the lead optimisation process to achieve higher selectivity within a given target family.

Explicit Diversity Index (EDI): a novel measure for assessing the diversity of compound databases.

A novel diversity assessment method, the Explicit Diversity Index (EDI), is introduced for druglike molecules. EDI combines structural and synthesis-related dissimilarity values and expresses them as a single number. As an easily interpretable measure, it facilitates the decision making in the design of combinatorial libraries, and it might assist in the comparison of compound sets provided by different manufacturers. Because of its rapid calculation algorithm, EDI enables the diversity assessment of in-house or commercial compound collections.

A neural network based classification scheme for cytotoxicity predictions:Validation on 30,000 compounds.

Elimination of cytotoxic compounds in the early phases of drug discovery can save substantial amounts of research and development costs. An artificial neural network based approach using atomic fragmental descriptors has been developed to categorize compounds according to their in vitro human cytotoxicity. Fragmental descriptors were obtained from the Atomic7 linear logP calculation method implemented in Pallas PrologP program. We used cytotoxicity values obtained from an in-house screening campaign of a diverse set of 30,000 drug-like molecules. The training set included only the most and least toxic 12,998 compounds, however, cytotoxicity data for all compounds were used for validation. The proposed approach can be safely used for filtering out potentially cytotoxic candidates from the development pipeline before synthesis or assays during lead development or lead optimisation. The trained neural network misclassified less than 5% percent of the non-toxic and 9% of the toxic compounds.

A neural network based prediction of octanol-water partition coefficients using atomic5 fragmental descriptors.

An artificial neural network based approach using Atomic5 fragmental descriptors has been developed to predict the octanol-water partition coefficient (logP). We used a pre-selected set of organic molecules from PHYSPROP database as training and test sets for a feedforward neural network. Results demonstrate the superiority of our non-linear model over the traditional linear method.