Design considerations and analysis planning of a phase 2a proof of concept study in rheumatoid arthritis in the presence of possible non-monotonicity

BACKGROUND: It is important to quantify the dose response for a drug in phase 2a clinical trials so the optimal doses can then be selected for subsequent late phase trials. In a phase 2a clinical trial of new lead drug being developed for the treatment of rheumatoid arthritis (RA), a U-shaped dose response curve was observed. In the light of this result further research was undertaken to design an efficient phase 2a proof of concept (PoC) trial for a follow-on compound using the lessons learnt from the lead compound. METHODS: The planned analysis for the Phase 2a trial for GSK123456 was a Bayesian Emax model which assumes the dose-response relationship follows a monotonic sigmoid "S" shaped curve. This model was found to be suboptimal to model the U-shaped dose response observed in the data from this trial and alternatives approaches were needed to be considered for the next compound for which a Normal dynamic linear model (NDLM) is proposed. This paper compares the statistical properties of the Bayesian Emax model and NDLM model and both models are evaluated using simulation in the context of adaptive Phase 2a PoC design under a variety of assumed dose response curves: linear, Emax model, U-shaped model, and flat response. RESULTS: It is shown that the NDLM method is flexible and can handle a wide variety of dose-responses, including monotonic and non-monotonic relationships. In comparison to the NDLM model the Emax model excelled with higher probability of selecting ED90 and smaller average sample size, when the true dose response followed Emax like curve. In addition, the type I error, probability of incorrectly concluding a drug may work when it does not, is inflated with the Bayesian NDLM model in all scenarios which would represent a development risk to pharmaceutical company. The bias, which is the difference between the estimated effect from the Emax and NDLM models and the simulated value, is comparable if the true dose response follows a placebo like curve, an Emax like curve, or log linear shape curve under fixed dose allocation, no adaptive allocation, half adaptive and adaptive scenarios. The bias though is significantly increased for the Emax model if the true dose response follows a U-shaped curve. CONCLUSIONS: In most cases the Bayesian Emax model works effectively and efficiently, with low bias and good probability of success in case of monotonic dose response. However, if there is a belief that the dose response could be non-monotonic then the NDLM is the superior model to assess the dose response

Analyzing high resolution topography for advancing the understanding of mass and energy transfer through landscapes: A review

International audienceThe study of mass and energy transfer across landscapes has recently evolved to comprehensive considerations acknowledging the role of biota and humans as geomorphic agents, as well as the importance of small-scale landscape features. A contributing and supporting factor to this evolution is the emergence over the last two decades of technologies able to acquire high resolution topography (HRT) (meter and sub-meter resolution) data. Landscape features can now be captured at an appropriately fine spatial resolution at which surface processes operate; this has revolutionized the way we study Earth-surface processes. The wealth of information contained in HRT also presents considerable challenges. For example, selection of the most appropriate type of HRT data for a given application is not trivial. No definitive approach exists for identifying and filtering erroneous or unwanted data, yet inappropriate filtering can create artifacts or eliminate/distort critical features. Estimates of errors and uncertainty are often poorly defined and typically fail to represent the spatial heterogeneity of the dataset, which may introduce bias or error for many analyses. For ease of use, gridded products are typically preferred rather than the more information-rich point cloud representations. Thus many users take advantage of only a fraction of the available data, which has furthermore been subjected to a series of operations often not known or investigated by the user. Lastly, standard HRT analysis work-flows are yet to be established for many popular HRT operations, which has contributed to the limited use of point cloud data.In this review, we identify key research questions relevant to the Earth-surface processes community within the theme of mass and energy transfer across landscapes and offer guidance on how to identify the most appropriate topographic data type for the analysis of interest. We describe the operations commonly performed from raw data to raster products and we identify key considerations and suggest appropriate work-flows for each, pointing to useful resources and available tools. Future research directions should stimulate further development of tools that take advantage of the wealth of information contained in the HRT data and address the present and upcoming research needs such as the ability to filter out unwanted data, compute spatially variable estimates of uncertainty and perform multi-scale analyses. While we focus primarily on HRT applications for mass and energy transfer, we envision this review to be relevant beyond the Earth-surface processes community for a much broader range of applications involving the analysis of HRT

The complete genome, comparative and functional analysis of Stenotrophomonas maltophilia reveals an organism heavily shielded by drug resistance determinants

The complete Stenotrophomonas maltophilia genome sequence suggests that it can act as a reservoir of antibiotic resistance determinants

Small-Molecule Ligands of Methyl-Lysine Binding Proteins: Optimization of Selectivity for L3MBTL3

Lysine methylation is a key epigenetic mark, the dysregulation of which is linked to many diseases. Small molecule antagonism of methyl-lysine (Kme) binding proteins that recognize such epigenetic marks can improve our understanding of these regulatory mechanisms and potentially validate Kme binding proteins as drug discovery targets. We previously reported the discovery of 1 (UNC1215), the first potent and selective small molecule chemical probe of a methyl-lysine reader protein, L3MBTL3, which antagonizes the mono- and dimethyl-lysine reading function of L3MBTL3. The design, synthesis, and structure activity relationship studies that led to the discovery of 1 are described herein. These efforts established the requirements for potent L3MBTL3 binding and enabled the design of novel antagonists, such as compound 2 (UNC1679), that maintain in vitro and cellular potency with improved selectivity against other MBT-containing proteins. The antagonists described were also found to effectively interact with unlabeled endogenous L3MBTL3 in cells

Exploiting an Allosteric Binding Site of PRMT3 Yields Potent and Selective Inhibitors

Protein arginine methyltransferases (PRMTs) play an important role in diverse biological processes. Among the nine known human PRMTs, PRMT3 has been implicated in ribosomal biosynthesis via asymmetric dimethylation of the 40S ribosomal protein S2 and in cancer via interaction with the DAL-1 tumor suppressor protein. However, few selective inhibitors of PRMTs have been discovered. We recently disclosed the first selective PRMT3 inhibitor, which occupies a novel allosteric binding site and is noncompetitive with both the peptide substrate and cofactor. Here we report comprehensive structure-activity relationship studies of this series, which resulted in the discovery of multiple PRMT3 inhibitors with submicromolar potencies. An X-ray crystal structure of compound 14u in complex with PRMT3 confirmed that this inhibitor occupied the same allosteric binding site as our initial lead compound. These studies provide the first experimental evidence that potent and selective inhibitors can be created by exploiting the allosteric binding site of PRMT3

Small-molecule ligands of methyl-lysine binding proteins: Optimization of selectivity for L3MBTL3

Proteins which bind methylated lysines (“readers” of the histone code) are important components in the epigenetic regulation of gene expression and can also modulate other proteins that contain methyl-lysine such as p53 and Rb. Recognition of methyl-lysine marks by MBT domains leads to compaction of chromatin and a repressed transcriptional state. Antagonists of MBT domains would serve as probes to interrogate the functional role of these proteins and initiate the chemical biology of methyl-lysine readers as a target class. Small molecule MBT antagonists were designed based on the structure of histone peptide-MBT complexes and their interaction with MBT domains determined using a chemiluminescent assay and ITC. The ligands discovered antagonize native histone peptide binding, exhibiting 5-fold stronger binding affinity to L3MBTL1 than its preferred histone peptide. The first co-crystal structure of a small molecule bound to L3MBTL1 was determined and provides new insights into binding requirements for further ligand design

Discovery of a 2,4-Diamino-7-aminoalkoxyquinazoline as a Potent and Selective Inhibitor of Histone Lysine Methyltransferase G9a

SAR exploration of the 2,4-diamino-6,7-dimethoxyquinazoline template led to the discovery of 8 (UNC0224) as a potent and selective G9a inhibitor. A high resolution X-ray crystal structure of the G9a-8 complex, the first co-crystal structure of G9a with a small molecule inhibitor, was obtained. The co-crystal structure validated our binding hypothesis and will enable structure-based design of novel inhibitors. 8 is a useful tool for investigating the biology of G9a and its roles in chromatin remodeling

Protein Lysine Methyltransferase G9a Inhibitors: Design, Synthesis, and Structure Activity Relationships of 2,4-Diamino-7-aminoalkoxy-quinazolines.

Protein lysine methyltransferase G9a, which catalyzes methylation of lysine 9 of histone H3 (H3K9) and lysine 373 (K373) of p53, is over expressed in human cancers. Genetic knockdown of G9a inhibits cancer cell growth and the di-methylation of p53 K373 results in the inactivation of p53. Initial SAR exploration of the 2,4-diamino-6,7-dimethoxyquinazoline template represented by 3a (BIX01294), a selective small molecule inhibitor of G9a and GLP, led to the discovery of 10 (UNC0224) as a potent G9a inhibitor with excellent selectivity. A high resolution X-ray crystal structure of the G9a-10 complex, the first co-crystal structure of G9a with a small molecule inhibitor, was obtained. Based on the structural insights revealed by this co-crystal structure, optimization of the 7-dimethylaminopropoxy side chain of 10 resulted in the discovery of 29 (UNC0321) (Morrison Ki = 63 pM), which is the first G9a inhibitor with picomolar potency and the most potent G9a inhibitor to date

Target 2035-update on the quest for a probe for every protein

Twenty years after the publication of the first draft of the human genome, our knowledge of the human proteome is still fragmented. The challenge of translating the wealth of new knowledge from genomics into new medicines is that proteins, and not genes, are the primary executers of biological function. Therefore, much of how biology works in health and disease must be understood through the lens of protein function. Accordingly, a subset of human proteins has been at the heart of research interests of scientists over the centuries, and we have accumulated varying degrees of knowledge about approximately 65% of the human proteome. Nevertheless, a large proportion of proteins in the human proteome (∼35%) remains uncharacterized, and less than 5% of the human proteome has been successfully targeted for drug discovery. This highlights the profound disconnect between our abilities to obtain genetic information and subsequent development of effective medicines. Target 2035 is an international federation of biomedical scientists from the public and private sectors, which aims to address this gap by developing and applying new technologies to create by year 2035 chemogenomic libraries, chemical probes, and/or biological probes for the entire human proteome