Chemometric-assisted cocrystallization: Supervised pattern recognition for predicting the formation of new functional cocrystals

Owing to the antimicrobial and insecticide properties, the use of natural compounds like essential oils and their active components has proven to be an effective alternative to synthetic chemicals in different fields ranging from drug delivery to agriculture and from nutrition to food preservation. Their limited application due to the high volatility and scarce water solubility can be expanded by using crystal engineering approaches to tune some properties of the active molecule by combining it with a suitable partner molecule (coformer). However, the selection of coformers and the experimental effort required for discovering cocrystals are the bottleneck of cocrystal engineering. This study explores the use of chemometrics to aid the discovery of cocrystals of active ingredients suitable for various applications. Partial Least Squares–Discriminant Analysis is used to discern cocrystals from binary mixtures based on the molecular features of the coformers. For the first time, by including failed cocrystallization data and considering a variety of chemically diverse compounds, the proposed method resulted in a successful prediction rate of 85% for the test set in the model validation phase and of 74% for the external test set

Improving Cognition to Increase Treatment Efficacy in Schizophrenia: Effects of Metabolic Syndrome on Cognitive Remediation's Outcome

Cognitive impairment, typically more severe in treatment resistant patients, is considered a hallmark of schizophrenia and the prime driver of functional disability. Recent evidence suggests that metabolic syndrome may contribute to cognitive deficits in schizophrenia, possibly through shared underlying mechanisms. However, results are still contradictory and no study has so far examined the influence of metabolic syndrome on cognitive outcome after cognitive remediation therapy (CRT). Based on these premises, this study aims to investigate the relationship between metabolic syndrome and cognition, specifically considering cognitive outcome after treatment. Secondary objectives include the analysis of the association between cognitive impairment and psychopathological status and, in a subgroup of patients, the evaluation of the effect of Sterol Regulatory Element Binding Transcription Factor 1 (SREBF-1) rs11868035 genetic polymorphism, previously associated with metabolic alterations, on both cognition and metabolic syndrome. One-hundred seventy-two outpatients with schizophrenia were assessed for metabolic parameters and neurocognitive measures and 138 patients, who completed CRT, were re-evaluated for cognition. A subsample of 51 patients was also genotyped for rs11868035 from peripheral blood sample. Results show a negative impact of metabolic syndrome on executive functions and global cognitive outcome after CRT. Data also revealed a significant effect of SREBF-1 polymorphism, with a higher prevalence of metabolic syndrome and worse processing speed performance among G/G homozygous subjects, compared the A allele carriers. Overall these findings support the hypothesis that metabolic alterations may hamper the capacity to restore cognitive deficits, as well as they highlight the need to further explore possible converging mechanisms underlying both cognitive and metabolic dysfunction. At the clinical level, results point to the importance of a comprehensive assessment including the metabolic status of patients and of individualized strategies addressing metabolic dysfunction in order to potentiate treatment outcome in schizophrenia

HRAS overexpression predicts response to Lenvatinib treatment in gastroenteropancreatic neuroendocrine tumors

IntroductionNeuroendocrine neoplasms (NENs) are a rare group of tumors exceptionally heterogeneous, with clinical presentation ranging from well differentiated more indolent tumors to poorly differentiated very aggressive forms. Both are often diagnosed after the metastatic spread and require appropriate medical treatment. A high priority need in the management of this disease is the identification of effective therapeutic strategies for advanced and metastatic patients. The recent TALENT trial demonstrated the efficacy of lenvatinib, a multi-tyrosine kinase inhibitor, in patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs) with no other treatment indication. Further development of this drug in advanced NETs is warranted.MethodsWe investigated potential clinical and molecular determinants of lenvatinib response in human primary cultures derived from patients with GEP-NET of different grades and sites of origin. We correlated response to treatment with patient clinical characteristics, with the mutational status of 161-cancer associated genes and with the expression levels of MKI-related genes.ResultsLenvatinib exerted a significant antitumor activity in primary GEP-NET cells, with median survival inhibitions similar or higher than those of standard frontline treatments. Of the 11 primary cultures analyzed in our case series, 6 were classified as responder showing a significant survival inhibition, and 5 as non-responder. We observed that the overexpression of HRAS in the original tumor tissue compared to the matched healthy tissue significantly correlated with responsiveness of primary cells to lenvatinib (p=.048). All 5 non-responder cultures showed normal HRAS expression, while of the 6 responder cultures, 4 had HRAS overexpression. Overexpression of HRAS was not associated with gene mutation. None of the other evaluated clinical variables (grade, Ki67, site of origin and syndromic disease) or molecular markers correlated with response.DiscussionLenvatinib appears to be a highly effective drug for the treatment of NETs. The evaluation of HRAS expression in the tumor tissue might improve patient selection and optimize therapeutic outcome

Application of experimental design in HPLC method optimisation for the simultaneous determination of multiple bioactive cannabinoids

The scientific interest in Cannabis sativa L. analysis has been rapidly increasing in recent years, especially for what concerns cannabinoids, plant secondary metabolites which are well known for having many biological properties. High-performance liquid chromatography (HPLC) is frequently used for both the qualitative and quantitative analysis of cannabinoids in plant extracts from C. sativa and its derived products. Many studies have been focused on the main cannabinoids, such as ∆9-tetrahydrocannabinolic acid (∆9-THCA), cannabidiolic acid (CBDA), cannabigerolic acid (CBGA) and their decarboxylated derivatives, such as ∆9-tetrahydrocannabinol (∆9-THC), cannabidiol (CBD) and cannabigerol (CBG). In addition to the abovementioned compounds, the plant produces other metabolites of the same chemical class, and some of them have shown interesting biological activities. In the light of this, it is important to have efficient analytical methods for the simultaneous separation of cannabinoids, which is quite complex since they present similar chemical-physical characteristics. The present work is focused on the use of the Design of Experiments technique (DoE) to develop and optimise an HPLC method for the simultaneous separation of 14 cannabinoids. Experimental design optimisation was applied by using a Central Composite Face-Centered design to achieve the best resolution with minimum experimental trials. Five significant variables affecting the chromatographic separation, including ammonium formate concentration, gradient elution, run time and flow rate, were studied. A multivariate strategy, based on Principal Component Analysis (PCA) and Partial Least Squared (PLS) regression, was used to define the best operative conditions. The developed method allowed for the separation of 12 out of 14 cannabinoids. Due to co-elution phenomena, HPLC coupled with a triple quadrupole mass analyser (HPLC-ESI-MS/MS) was applied, monitoring the specific transitions of each compound in the multiple reaction monitoring (MRM) mode. Finally, the optimised method was applied to C. sativa extracts having a different cannabinoid profile to demonstrate its efficiency to real samples. The methodology applied in this study can be useful for the separation of other cannabinoid mixtures, by means of appropriate optimisation of the experimental conditions

Communicative-pragmatic abilities mediate the relationship between cognition and daily functioning in schizophrenia

Pragmatics refers to the capacity to understand the speaker's meaning and thus to appropriately engage in a conversation. This study aims at establishing the role of communicative-pragmatic abilities in functioning, defined as a set of daily activities, in schizophrenia. This would contribute to enrich current models of the neurocognitive predictors of functioning, which have so far neglected pragmatics. One hundred people with schizophrenia underwent a comprehensive assessment including functioning, cognition, theory of mind (ToM), and pragmatics. We tested the effects of cognition as a predictor of functioning, first mediated by ToM, then sequentially mediated by ToM and pragmatics. Next, we explored the predictive effect of cognition, sequentially mediated by ToM and pragmatics, on different functional domains (i.e., interpersonal relations, instrumental role, and personal autonomy). The first model confirmed that ToM acts as a mediator between cognition and functioning. Importantly, the second model highlighted also the main mediating role of pragmatics. The mediation models on different functional domains showed that, when considered together, both pragmatics and ToM significantly influenced all aspects of functioning. When considered separately, pragmatics was significantly related to interpersonal functioning, while ToM to personal autonomy. Innovatively, our findings highlight that pragmatics has a main role, both direct and indirect, in affecting functioning. Of particular interest is that the impact of pragmatics encompasses different functional domains, and especially interpersonal functioning. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

Disentangling Cognitive Heterogeneity in Psychotic Spectrum Disorders

•For each cognitive task, roughly 40% of patients displayed poor performance.•Cluster analysis showed 3 cognitive profiles with different degrees of impairment.•Verbal IQ and diagnosis were identified as predictors of cluster membership.•Explaining cognitive heterogeneity is needed to develop better-suited interventions. Neuropsychological impairments represent a central feature of psychosis-spectrum disorders. It is characterized by a great both within- and between-subjects variability (i.e. cognitive heterogeneity), which needs to be better disentangled. The present study aimed to describe the distribution of performance on the Brief Assessment of Cognition in Schizophrenia (BACS) by using the Equivalent Scores, in order to balance statistical methodological problems. To do so, cognitive performance groups were branded, identifying the main factors contributing to cognitive heterogeneity. A sample of 583 patients with a diagnosis of Schizophrenia or Psychotic Disorder Not Otherwise Specified was enrolled and assessed for neurocognition and intellectual level. K-means cluster analysis was performed based on BACS Equivalent Scores. Differences among clusters were analyzed throughout Analysis of Variance and Discriminant Function Analysis in order to identify the most significant predictors of cluster membership. For each cognitive task, roughly 40% of patients displayed poor performance, while up to 63% displayed a symbol-coding deficit. K-means cluster analysis depicted three profiles characterized by “near-normal” cognition, widespread impairment, and “borderline” profile. Discriminant analysis selected Verbal IQ and diagnosis as predictors of cluster membership. Our findings support the usefulness of Equivalent Scores and cluster analysis to explain cognitive heterogeneity, and tailor better interventions