Diagnosing solubility limitations – the example of hydrate formation

Solubility is regarded as one of the key challenges in many drug discovery projects. Thus, it is essential to support lead finding and optimization efforts with appropriate solubility data. In silico solubility prediction remains challenging and therefore a screening assay is used as a first filter, followed by selected follow-up assays to reveal what causes the low solubility of a specific compound or chemotype. Results from diagnosing the underlying reason for solubility limitation are discussed. As lipophilicity and crystal lattice forces are regarded as main contributors to limiting solubility, changes in solid state are important to be recognized. Solubility limitation by various factors will be presented and the impact of the solid-state is exemplified by compounds that are able to form hydrates

Simulated rat intestinal fluid improves oral exposure prediction for poorly soluble compounds over a wide dose range

Solubility can be the absorption limiting factor for drug candidates and is therefore a very important input parameter for oral exposure prediction of compounds with limited solubility. Biorelevant media of the fasted and fed state have been published for humans, as well as for dogs in the fasted state. In a drug discovery environment, rodents are the most common animal model to assess the oral exposure of drug candidates. In this study a rat simulated intestinal fluid (rSIF) is proposed as a more physiologically relevant media to describe drug solubility in rats. Equilibrium solubility in this medium was tested as input parameter for physiologically-based pharmacokinetics (PBPK) simulations of oral pharmacokinetics in the rat. Simulations were compared to those obtained using other solubility values as input parameters, like buffer at pH 6.8, human simulated intestinal fluid and a comprehensive dissolution assay based on rSIF. Our study on nine different compounds demonstrates that the incorporation of rSIF equilibrium solubility values into PBPK models of oral drug exposure can significantly improve the reliability of simulations in rats for doses up to 300 mg/kg compared to other media. The comprehensive dissolution assay may help to improve further simulation outcome, but the greater experimental effort as compared to equilibrium solubility may limit its use in a drug discovery environment. Overall, PBPK simulations based on solubility in the proposed rSIF medium can improve prioritizing compounds in drug discovery as well as planning dose escalation studies, e.g. during toxicological investigations

Possible evidence for the breakdown of the CKM-paradigm of CP-violation

Using primarily experimental inputs for S(B -> psi Ks), Delta M_Bs, Delta M_Bd, BR(B -> tau nu) and epsilon_K along with necessary inputs from the lattice, we find that the measured value of sin(2 beta) is smaller than expectations of the Standard Model by as much as 3.3 sigma, and also that the measured value of the BR(B -> tau nu) seems to be less than the predicted value by about 2.8 sigma. However, through a critical study we show that most likely the dominant source of these deviations is in B_d(s) mixings and in sin(2 beta) and less so in B -> tau nu, and also that the bulk of the problem persists even if input from epsilon_K is not used. The fact that kaon mixing and epsilon_K are not the dominant source of the deviation from the Standard Model has the very important consequence that model independent considerations imply that the scale of the relevant new CP-violating physics is below O(2 TeV), thus suggesting that direct signals of the new particle(s) may well be accessible in collider experiments at the LHC and perhaps even at the Tevatron.Comment: 6 pages, 8 figures. Added another fit and some more discussion showing that BR(B->tau nu) comes out in good agreement with predictions of the SM if the measured value of sin2beta is not used as an input. References adde

Routine molecular profiling of cancer: results of a one-year nationwide program of the French Cooperative Thoracic Intergroup (IFCT) for advanced non-small cell lung cancer (NSCLC) patients.

International audienceBackground: The molecular profiling of patients with advanced non-small-cell lung cancer (NSCLC) for known oncogenic drivers is recommended during routine care. Nationally, however, the feasibility and effects on outcomes of this policy are unknown. We aimed to assess the characteristics, molecular profiles, and clinical outcomes of patients who were screened during a 1-year period by a nationwide programme funded by the French National Cancer Institute. Methods This study included patients with advanced NSCLC, who were routinely screened for EGFR mutations, ALK rearrangements, as well as HER2 (ERBB2), KRAS, BRAF, and PIK3CA mutations by 28 certified regional genetics centres in France. Patients were assessed consecutively during a 1-year period from April, 2012, to April, 2013. We measured the frequency of molecular alterations in the six routinely screened genes, the turnaround time in obtaining molecular results, and patients' clinical outcomes. This study is registered with ClinicalTrials.gov, number NCT01700582. Findings 18 679 molecular analyses of 17 664 patients with NSCLC were done (of patients with known data, median age was 64·5 years [range 18–98], 65% were men, 81% were smokers or former smokers, and 76% had adenocarcinoma). The median interval between the initiation of analysis and provision of the written report was 11 days (IQR 7–16). A genetic alteration was recorded in about 50% of the analyses; EGFR mutations were reported in 1947 (11%) of 17 706 analyses for which data were available, HER2 mutations in 98 (1%) of 11 723, KRAS mutations in 4894 (29%) of 17 001, BRAF mutations in 262 (2%) of 13 906, and PIK3CA mutations in 252 (2%) of 10 678; ALK rearrangements were reported in 388 (5%) of 8134 analyses. The median duration of follow-up at the time of analysis was 24·9 months (95% CI 24·8–25·0). The presence of a genetic alteration affected first-line treatment for 4176 (51%) of 8147 patients and was associated with a significant improvement in the proportion of patients achieving an overall response in first-line treatment (37% [95% CI 34·7–38·2] for presence of a genetic alteration vs 33% [29·5–35·6] for absence of a genetic alteration; p=0·03) and in second-line treatment (17% [15·0–18·8] vs 9% [6·7–11·9]; p<0·0001). Presence of a genetic alteration was also associated with improved first-line progression-free survival (10·0 months [95% CI 9·2–10·7] vs 7·1 months [6·1–7·9]; p<0·0001) and overall survival (16·5 months [15·0–18·3] vs 11·8 months [10·1–13·5]; p<0·0001) compared with absence of a genetic alteration. Interpretation Routine nationwide molecular profiling of patients with advanced NSCLC is feasible. The frequency of genetic alterations, acceptable turnaround times in obtaining analysis results, and the clinical advantage provided by detection of a genetic alteration suggest that this policy provides a clinical benefit

Human Iron−Sulfur Cluster Assembly, Cellular Iron Homeostasis, and Disease†

ABSTRACT: Iron-sulfur (Fe-S) proteins contain prosthetic groups consisting of two or more iron atoms bridged by sulfur ligands, which facilitate multiple functions, including redox activity, enzymatic function, and maintenance of structural integrity. More than 20 proteins are involved in the biosynthesis of iron-sulfur clusters in eukaryotes. Defective Fe-S cluster synthesis not only affects activities of many iron-sulfur enzymes, such as aconitase and succinate dehydrogenase, but also alters the regulation of cellular iron homeostasis, causing both mitochondrial iron overload and cytosolic iron deficiency. In this work, we review human Fe-S cluster biogenesis and human diseases that are caused by defective Fe-S cluster biogenesis. Fe-S cluster biogenesis takes place essentially in every tissue of humans, and products of human disease genes, including frataxin, GLRX5, ISCU, and ABCB7, have important roles in the process. However, the human diseases, Friedreich ataxia, glutaredoxin 5-deficient sideroblastic anemia, ISCU myopathy, and ABCB7 sideroblastic anemia/ataxia syndrome, affect specific tissues, while sparing others. Here we discuss the phenotypes caused by mutations in these different disease genes, and we compare the underlying pathophysiology and discuss the possible explanations for tissue-specific pathology in these diseases caused by defective Fe-S cluster biogenesis. HUMAN CELLULAR IRON HOMEOSTASI

Track A Basic Science

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138319/1/jia218438.pd

QCD and strongly coupled gauge theories : challenges and perspectives

We highlight the progress, current status, and open challenges of QCD-driven physics, in theory and in experiment. We discuss how the strong interaction is intimately connected to a broad sweep of physical problems, in settings ranging from astrophysics and cosmology to strongly coupled, complex systems in particle and condensed-matter physics, as well as to searches for physics beyond the Standard Model. We also discuss how success in describing the strong interaction impacts other fields, and, in turn, how such subjects can impact studies of the strong interaction. In the course of the work we offer a perspective on the many research streams which flow into and out of QCD, as well as a vision for future developments.Peer reviewe

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery