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Deficits in information processing and cognition are among the most robust findings in schizophrenia patients. Previous efforts to translate group-level deficits into clinically relevant and individualized information have, however, been non-successful, which is possibly explained by biologically different disease subgroups. We applied machine learning algorithms on measures of electrophysiology and cognition to identify potential subgroups of schizophrenia. Next, we explored subgroup differences regarding treatment response. Sixty-six antipsychotic-naive first-episode schizophrenia patients and sixty-five healthy controls underwent extensive electrophysiological and neurocognitive test batteries. Patients were assessed on the Positive and Negative Syndrome Scale (PANSS) before and after 6 weeks of monotherapy with the relatively selective D2 receptor antagonist, amisulpride (280.3±159 mg per day). A reduced principal component space based on 19 electrophysiological variables and 26 cognitive variables was used as input for a Gaussian mixture model to identify subgroups of patients. With support vector machines, we explored the relation between PANSS subscores and the identified subgroups. We identified two statistically distinct subgroups of patients. We found no significant baseline psychopathological differences between these subgroups, but the effect of treatment in the groups was predicted with an accuracy of 74.3% (P=0.003). In conclusion, electrophysiology and cognition data may be used to classify subgroups of schizophrenia patients. The two distinct subgroups, which we identified, were psychopathologically inseparable before treatment, yet their response to dopaminergic blockade was predicted with significant accuracy. This proof of principle encourages further endeavors to apply data-driven, multivariate and multimodal models to facilitate progress from symptom-based psychiatry toward individualized treatment regimens.

Udgivelsesdato: April 2006, Tiamulin is a pleuromutilin antibiotic that is used in veterinary medicine. The recently published crystal structure of a tiamulin-50S ribosomal subunit complex provides detailed information about how this drug targets the peptidyl transferase center of the ribosome. To promote rational design of pleuromutilin-based drugs, the binding of the antibiotic pleuromutilin and three semisynthetic derivatives with different side chain extensions has been investigated using chemical footprinting. The nucleotides A2058, A2059, G2505, and U2506 are affected in all of the footprints, suggesting that the drugs are similarly anchored in the binding pocket by the common tricyclic mutilin core. However, varying effects are observed at U2584 and U2585, indicating that the side chain extensions adopt distinct conformations within the cavity and thereby affect the rRNA conformation differently. An Escherichia coli L3 mutant strain is resistant to tiamulin and pleuromutilin, but not valnemulin, implying that valnemulin is better able to withstand an altered rRNA binding surface around the mutilin core. This is likely due to additional interactions made between the valnemulin side chain extension and the rRNA binding site. The data suggest that pleuromutilin drugs with enhanced antimicrobial activity may be obtained by maximizing the number of interactions between the side chain moiety and the peptidyl transferase cavity.