The aetiology of polycystic ovary syndrome (PCOS) remains unknown. This familial syndrome is prevalent among reproductive-aged women and its inheritance indicates a dominant regulatory gene with incomplete penetrance. However, promising candidate genes have proven unreliable as markers for the PCOS phenotype. This lack of genetic linkage may represent both extreme heterogeneity of PCOS and difficulty in establishing a universally accepted PCOS diagnosis. Nevertheless, hyperandrogenism is one of the most consistently expressed PCOS traits. Animal models that mimic fetal androgen excess may thus provide unique insight into the origins of the PCOS syndrome. Many female mammals exposed to androgen excess in utero or during early post-natal life typically show masculinized and defeminized behaviour, ovulatory dysfunction and virilized genitalia, although behavioural and ovulatory dysfunction can coexist without virilized genitalia based upon the timing of androgen excess. One animal model shows particular relevance to PCOS: the prenatally androgenized female rhesus monkey. Females exposed to androgen excess early in gestation exhibit hyperandrogenism, oligomenorrhoea and enlarged, polyfollicular ovaries, in addition to LH hypersecretion, impaired embryo development, insulin resistance accompanying abdominal obesity, impaired insulin response to glucose and hyperlipidaemia. Female monkeys exposed to androgen excess late in gestation mimic these programmed changes, except for LH and insulin secretion defects. In utero androgen excess may thus variably perturb multiple organ system programming and thereby provide a single, fetal origin for a heterogeneous adult syndrome.

/pdf/androgen-excess-fetal-programming-of-female-reproduction-a-mqye2c2d9j.pdf

Androgen excess fetal programming of female reproduction: a developmental aetiology for polycystic ovary syndrome?

Context: The polycystic ovary syndrome (PCOS) is the commonest endocrine abnormality in women of reproductive age. Objective: To determine the rate of hospital admissions for women with PCOS in Western Australian population in comparison to women without PCOS. Design: A population-based retrospective cohort study using data linkage in a statewide hospital morbidity database system. Setting: All hospitals within Western Australia. Participants: A total of 2566 women with PCOS hospitalized from 1997–2011 and 25 660 randomly selected age-matched women without a PCOS diagnosis derived from the electoral roll. Main Outcome Measures: Hospitalizations by ICD-10-M diagnoses from 15 years were compared. Results: Hospitalizations were followed until a median age of 35.8 years (interquartile range, 31.0–39.9). PCOS was associated with more nonobstetric and non-injury-related hospital admissions (median, 5 vs 2; P < .001), a diagnosis of adult-onset diabetes (12.5 vs 3.8%), obesity (16.0 vs 3.7%), hypertensive disord...

The potential implications of a PCOS diagnosis on a woman’s long-term health using data linkage

Bcr-Abl inhibitors paved the way of targeted therapy epoch. Imatinib was the first tyrosine kinase inhibitor to be discovered with high specificity for Bcr-Abl protein resulting from t(9, 22)-derived Philadelphia chromosome. Although the specific targeting of that oncoprotein, several Bcr-Abl-dependent and Bcr-Abl-independent mechanisms of resistance to imatinib arose after becoming first-line therapy in chronic myelogenous leukemia (CML) treatment. Consequently, new specific drugs, namely dasatinib, nilotinib, bosutinib, and ponatinib, were rationally designed and approved for clinic to override resistances. Imatinib fine mechanisms of action had been elucidated to rationally develop those second- and third-generation inhibitors. Crystallographic and structure-activity relationship analysis, jointly to clinical data, were pivotal to shed light on this topic. More recently, preclinical evidence on bafetinib, rebastinib, tozasertib, danusertib, HG-7-85-01, GNF-2, and 1,3,4-thiadiazole derivatives lay promising foundations for better inhibitors to be approved for clinic in the near future. Notably, structural mechanisms of action and drug design exemplified by Bcr-Abl inhibitors have broad relevance to both break through resistances in CML treatment and develop inhibitors against other kinases as targeted chemotherapeutics.

/pdf/past-present-and-future-of-bcr-abl-inhibitors-from-chemical-1gfnc7z8fo.pdf

Past, present, and future of Bcr-Abl inhibitors: from chemical development to clinical efficacy.

A risk prediction model is a statistical tool for estimating the probability that a currently healthy individual with specific risk factors will develop a condition in the future such as breast cancer. Reliably accurate prediction models can inform future disease burdens, health policies and individual decisions. Breast cancer prediction models containing modifiable risk factors, such as alcohol consumption, BMI or weight, condom use, exogenous hormone use and physical activity, are of particular interest to women who might be considering how to reduce their risk of breast cancer and clinicians developing health policies to reduce population incidence rates. We performed a systematic review to identify and evaluate the performance of prediction models for breast cancer that contain modifiable factors. A protocol was developed and a sensitive search in databases including MEDLINE and EMBASE was conducted in June 2010. Extensive use was made of reference lists. Included were any articles proposing or validating a breast cancer prediction model in a general female population, with no language restrictions. Duplicate data extraction and quality assessment were conducted. Results were summarised qualitatively, and where possible meta-analysis of model performance statistics was undertaken. The systematic review found 17 breast cancer models, each containing a different but often overlapping set of modifiable and other risk factors, combined with an estimated baseline risk that was also often different. Quality of reporting was generally poor, with characteristics of included participants and fitted model results often missing. Only four models received independent validation in external data, most notably the 'Gail 2' model with 12 validations. None of the models demonstrated consistently outstanding ability to accurately discriminate between those who did and those who did not develop breast cancer. For example, random-effects meta-analyses of the performance of the 'Gail 2' model showed the average C statistic was 0.63 (95% CI 0.59-0.67), and the expected/observed ratio of events varied considerably across studies (95% prediction interval for E/O ratio when the model was applied in practice was 0.75-1.19). There is a need for models with better predictive performance but, given the large amount of work already conducted, further improvement of existing models based on conventional risk factors is perhaps unlikely. Research to identify new risk factors with large additionally predictive ability is therefore needed, alongside clearer reporting and continual validation of new models as they develop.

A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance.

Synthetic heterocyclic candidates as promising α-glucosidase inhibitors: An overview.

The 1,3,4-thiadiazole nucleus is one of the most important and well-known heterocyclic nuclei, which is a common and integral feature of a variety of natural products and medicinal agents. Thiadiazole nucleus is present as a core structural component in an array of drug categories such as antimicrobial, anti-inflammatory, analgesic, antiepileptic, antiviral, antineoplastic, and antitubercular agents. The broad and potent activity of thiadiazole and their derivatives has established them as pharmacologically significant scaffolds. In this study, an attempt has been made with recent research findings on this nucleus, to review the structural modifications on different thiadiazole derivatives for various pharmacological activities.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/cbdd.12125

1,3,4-thiadiazole and its derivatives: a review on recent progress in biological activities.

Neurodegenerative disorders are consequences of progressive and irreversible loss of neurons due to unwanted apoptosis which involves caspases, a group of cysteine proteases that cleave other proteins and inactivate them. Among several different groups of caspase enzymes, caspases-3 plays a key role in apoptosis and are a therapeutic target for their inhibition. In pursuit of better caspase-3 inhibitors, a quantitative structure-activity relationship (QSAR) analysis was performed on a series of 1,3-dioxo-4-methyl-2,3-dihydro-1H-pyrrolo[3,4-c] quinolines as caspase-3 inhibitors using WIN CAChe 6.1 and Medicinal Chemistry Regression Machine. The best QSAR model was selected and validated by internal and external validation method. The values of statistical data are r = 0.955, F = 72.95, SEE = 0.397, q2 = 0.885, SPRESS = 0.44. The present study reveals that when the conformational minimum energy is increased, and lowest unoccupied molecular orbital energy and highest occupied molecular orbital energy are dec...

Prediction of caspase-3 inhibitory activity of 1,3-dioxo-4-methyl-2,3-dihydro-1h-pyrrolo[3,4-c] quinolines: QSAR study.

Neurodegenerative disorders are major consequences of excessive apoptosis caused by a proteolytic enzyme known as caspase-3. Therefore, caspase-3 inhibition has become a validated therapeutic approach for neurodegenerative disorders. We performed pharmacophore modeling on some synthetic derivatives of caspase-3 inhibitors (pyrrolo[3,4-c]quinoline-1,3-diones) using PHASE 3.0. This resulted in the common pharmacophore hypothesis AAHRR.6 which might be responsible for the biological activity: two aromatic rings (R) mainly in the quinoline nucleus, one hydrophobic (H) group (CH3), and two acceptor (A) groups (–C=O). After identifying a valid hypothesis, we also developed an atom-based 3D-QSAR model applying the PLS algorithm. The developed model was statistically robust (q2 = 0.53; pred_r2 = 0.80). Additionally, we have performed molecular docking studies, cross-validated our results, and gained a deeper insight into its molecular recognition process. Our developed model may serve as a query tool for future virtual screening and drug designing for this particular target.

/pdf/pharmacophore-modeling-and-docking-studies-on-some-4cul1p01ac.pdf

Pharmacophore modeling and docking studies on some nonpeptide-based caspase-3 inhibitors.

Seed rate and weed management on yield and nutrient uptake of wheat (Triticum aestivum)

Neurodegenerative disorders are consequences of progressive and irreversible loss of neurons due to unwanted apoptosis, which involves caspases, a group of cystein proteases that cleave other proteins and inactivate them. Among several different groups of caspase enzymes, caspases-3 play a key role in apoptosis. Therefore they are used as therapeutic target for their inhibitors. In pursuit of better caspase-3 inhibitors, a quantitative structure–activity relationship analysis was performed on a series of 1,3-dioxo-4-methyl-2, 3-dihydro-1H-pyrrolo [3,4-c] quinolines as caspase-3 inhibitors using WIN CAChe 6.1 and Medicinal Chemistry Regression Machine. The best QSAR model was selected and validated. The values of the statistical data are r = 0.951, F = 65.62, SEE = 0.4175, t = 2.08. The study reveals that, if the partition coefficient (log P), conformational minimum energy (CME), and the lowest unoccupied molecular orbital energy (E
 LUMO) are altered, the biological activity is increased. On the basis of the selected QSAR model, we designed a new series of compounds, calculated their activity, and found that the compounds were more potent than existing compounds.

QSAR study of 1,3–dioxo-4-methyl-2,3-dihydro-1h-pyrrolo[3,4-c]quinolines as caspase-3 inhibitors

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