Biomedical subjects

David J Diller

Publications and source records attributed to David J Diller.

9 recordsLinked to original sources

PubMed2006

Discovery and characterization of triaminotriazine aniline amides as highly selective p38 kinase inhibitors.

The p38 mitogen-activated protein (MAP) kinases are a family of serine/threonine protein kinases that play important roles in cellular responses to inflammation and external stress. Inhibitors of the p38 MAP kinase have shown promise for potential treatment of inflammatory disorders such as rheumatoid arthritis, acute coronary syndrome, psoriasis, and Crohn's disease. We identified a novel class of p38 inhibitors via high-throughput screening. PS200981 [3-(4-(1,4-diazepan-1-yl)-6-(((1S,2R,5S)-6,6-dimethylbicyclo[3.1.1]heptan-2-yl)methylamino)-1,3,5-triazin-2-ylamino)-4-methylbenzamide], a representative compound identified from screening a collection of combinatorial libraries, amounting to 2.1 million compounds, inhibits p38alpha kinase and the lipopolysaccharide (LPS)-induced increase in tumor necrosis factor (TNF) alpha levels in cell media of human monocytes with IC50 values of 1 microM. The screening data revealed a preferred synthon, 3-amino-4-methyl benzamide, which is critical for the activity against p38. This synthon appeared almost exclusively in screening hits including PS200981, and slight variations of this synthon including 3-amino benzamide and 2-amino-4-methyl benzamide also contained in the library were inactive. PS200981 is equally potent against the alpha and beta forms of p38 but did not inhibit p38 gamma and is >25-fold selective versus a panel of other kinases. PS200981 inhibited the LPS-induced increase in TNFalpha levels when administered at 30 mg/kg to mice. Selectivity and in vivo activity of this class of p38 inhibitors was further demonstrated by PS166276 [(R)-3-(4-(isobutyl(methyl)-amino)-6-(pyrrolidin-3-ylamino)-1,3,5-triazin-2-ylamino)-4-methylbenzamide], a highly structurally related but more potent and less cytotoxic inhibitor, in several intracellular signaling assays, and in LPS-challenged mice. Overall, this novel class of p38 inhibitors is potent, active in vitro and in vivo, and is highly selective.

Adenosine Triphosphate↗

PubMed2006

Successful screening of large encoded combinatorial libraries leading to the discovery of novel p38 MAP kinase inhibitors.

Screening of more than 2 million compounds comprising 41 distinct encoded combinatorial libraries revealed a novel structural class of p38 mitogen-activated protein (MAP) kinase inhibitors. The methodology used for screening large encoded combinatorial libraries combined with the statistical interpretation of screening results is described. A strong preference for a particular triaminotriazine aniline amide was discovered based on biological activity observed in the screening campaign. Additional screening of a focused follow-up combinatorial library yielded data expanding the unique combinatorial SAR and emphasizing an extraordinary preference for this particular building block and structural class. The preference is further highlighted when the p38 inhibitor data set is compared to data obtained for a panel of other kinases.

Combinatorial Chemistry Techniques↗

PubMed2005

Fast small molecule similarity searching with multiple alignment profiles of molecules represented in one-dimension.

Multiple sequence alignment has proven to be a powerful method for creating protein and DNA sequence alignment profiles. These profiles of protein families are useful tools for identifying conserved motifs, such as the catalytic triad of the serine protease family or the seven transmembrane helices of the G-protein coupled receptor family. Ultimately, the understanding of the critical motifs within a family is useful for identifying new members of the family. Due to the complexity of protein-ligand recognition, no universally accepted method exists for clustering small molecules into families with the same or similar biological activity. A combination of the concept of multiple sequence alignment and the 1-dimensional molecular representation described earlier offers a new method for profiling sets of small molecules with the same biological activity. These small molecule profiles can isolate key commonalities within the set of bioactive compounds much like a multiple sequence alignment can isolate critical motifs within a protein family. The small molecule profiles then make useful tools for searching small molecule databases for new compounds with the same biological activity. The technique is demonstrated here using the human ether-a-go-go potassium channel and the kinase SRC.

Algorithms↗

PubMed2005

The discovery of novel chemotypes of p38 kinase inhibitors.

In the late 1970s and the early 1980s the initial p38 chemotype, the triaryl imidazoles, was discovered as an off-target effect during the development of cyclooxygenase and 5-lipoxygenase inhibitors long before the identity of the p38 kinase was known. During the last 10 years a number of novel p38 chemotypes were discovered via high throughput screening. More recently, the first series of p38 inhibitors discovered by xray crystallographic and virtual screening was announced. Finally, throughout the life span of p38 drug discovery programs significant medicinal chemistry effort has continually been placed on the design of new inhibitors from known chemotypes using molecular modeling, protein crystallography, hybrid design and simply sound intuition. Indeed, the search for p38 kinase inhibitors offers an excellent historical perspective as to how technological changes that have taken place in the pharmaceutical industry over the last decade, have affected the ways in which new leads are discovered and advanced. It is the intent of this review to highlight the discoveries of novel p38 chemotypes, emphasizing where possible the key technologies used in the discoveries and the knowledge gained from each discovery.

Anti-Inflammatory Agents↗

PubMed2004

Deriving knowledge through data mining high-throughput screening data.

Deriving general knowledge from high-throughput screening data is made difficult by the significant amount of noise, arising primarily from false positives, in the data. The paradigm established for screening an encoded combinatorial library on polymeric support, an ECLiPS library, has a significant amount of built-in redundancy. Because of this redundancy, the resulting data can be interpreted through a rigorous statistical analysis procedure, thereby significantly reducing the number of false positives. Here, we develop the statistical models used to analyze data from high-throughput screens of ECLiPS libraries to derive unbiased true hit rates. These hit rates can also be calculated on subsets of the collection such as those compounds containing a carboxylic acid or those with molecular weight below 350 Da. The relative value of the hit rate on the subset of the collection can then be compared to the overall hit rate to determine the effect of the substructure or physical property on the likelihood of a molecule having biological activity. Here, we show the effects that various functional groups and the standard physical properties, molecular weight, hydrogen bond donors, hydrogen bond acceptors, log P, and rotatable bonds, have on the likelihood of a compound being biologically active. To our knowledge this is the first published account of the use of high-throughput screening data to elucidate the effects of physical properties and substructures on the likelihood of compounds showing biological activity over a broad range of pharmaceutically relevant targets.

Algorithms↗

PubMed2003

Kinases, homology models, and high throughput docking.

With the many protein sequences coming from the genome sequencing projects, it is unlikely that we will ever have an atomic resolution structure of every relevant protein. With high throughput crystallography, however, we will soon have representative structures for the vast majority of protein families. Thus the drug discovery and design process will rely heavily on protein modeling to address issues such as designing combinatorial libraries for an entire class of targets and engineering genome-wide selectivity over a target class. In this study we assess the value of high throughput docking into homology models. To do this we dock a database of random compounds seeded with known inhibitors into homology models of six different kinases. In five of the six cases the known inhibitors were found to be enriched by factors of 4-5 in the top 5% of the overall scored and ranked compounds. Furthermore, in the same five cases the known inhibitors were found to be enriched by factors of 2-3 in the top 5% of the scored and ranked known kinase inhibitors, thus showing that the homology models can pick up some of the crucial selectivity information.

Catalytic Domain↗

PubMed2002

Computation of the physio-chemical properties and data mining of large molecular collections.

Very large data sets of molecules screened against a broad range of targets have become available due to the advent of combinatorial chemistry. This information has led to the realization that ADME (absorption, distribution, metabolism, and excretion) and toxicity issues are important to consider prior to library synthesis. Furthermore, these large data sets provide a unique and important source of information regarding what types of molecular shapes may interact with specific receptor or target classes. Thus, the requirement for rapid and accurate data mining tools became paramount. To address these issues Pharmacopeia, Inc. formed a computational research group, The Center for Informatics and Drug Discovery (CIDD).* In this review we cover the work done by this group to address both in silico ADME modeling and data mining issues faced by Pharmacopeia because of the availability of a large and diverse collection (over 6 million discrete compounds) of drug-like molecules. In particular, in the data mining arena we discuss rapid docking tools and how we employ them, and we describe a novel data mining tool based on a ID representation of a molecule followed by a molecular sequence alignment step. For the ADME area we discuss the development and application of absorption, blood-brain barrier (BBB) and solubility models. Finally, we summarize the impact the tools and approaches might have on the drug discovery process.

Algorithms↗

PubMed2002

Can we separate active from inactive conformations?

Molecular modeling methodologies such as molecular docking, pharmacophore modeling, and 3D-QSAR, rely on conformational searches of small molecules as a starting point. All of these methodologies seek conformations of the small molecules as they bind to target proteins, i.e., their active conformations. Thus the question as to whether active conformations can be separated from inactive conformations is extremely relevant. In this paper, 3D-descriptors that separate random conformations from active conformations of small molecules are sought. To select appropriate descriptors, 65 protein-ligand complexes were taken from the protein data bank. For each ligand the active conformation was compared to randomly generated low energy conformations. Descriptors such as solvent accessible surface area, number of internal interactions and radius of gyration appear to be useful for separating the active conformations from the random conformations. The results with all these descriptors indicate that active conformations are less compact that random conformations, i.e., they have more solvent accessible surface area, fewer internal interactions and a larger radius of gyration than random conformations. Thus these descriptors could be useful as weights to bias conformational search procedures to conformations more likely to bind to proteins or as filters to eliminate conformations unlikely to bind to any protein.

Computer Simulation↗

PubMedDate not supplied

Use of catalyst pharmacophore models for screening of large combinatorial libraries.

Using a data set comprised of literature compounds and structure-activity data for cyclin dependent kinase 2, several pharmacophore hypotheses were generated using Catalyst and evaluated using several criteria. The two best were used in retrospective searches of 10 three-dimensional databases containing over 1,000,000 proprietary compounds. The results were then analyzed for the efficiency with which the hypotheses performed in the areas of compound prioritization, library prioritization, and library design. First as a test of their compound prioritization capabilities, the pharmacophore models were used to search combinatorial libraries that were known to contain CDK active compounds to see if the pharmacophore models could selectively choose the active compounds over the inactive compounds. Second as a test of their utility in library design again the pharmacophore models were used to search the active combinatorial libraries to see if the key synthons were over represented in the hits from the pharmacophore searches. Finally as a test of their ability to prioritize combinatorial libraries, several inactive libraries were searched in addition to the active libraries in order to see if the active libraries produced significantly more hits than the inactive libraries. For this study the pharmacophore models showed potential in all three areas. For compound prioritization, one of the models selected active compounds at a rate nearly 11 times that of random compound selection though in other cases models missed the active compounds entirely. For library design, most of the key fragments were over represented in the hits from at least one of the searches though again some key fragments were missed. Finally, for library prioritization, the two active libraries both produced a significant number of hits with both pharmacophore models, whereas none of the eight inactive libraries produced a significant number of hits for both models.

Amino Acid Sequence↗