Direct Phenotypic Screening in Mice: Identification of Individual, Novel Antinociceptive Compounds from a Library of 734 821 Pyrrolidine Bis-piperazines

The hypothesis in the current study is that the simultaneous direct in vivo testing of thousands to millions of systematically arranged mixture-based libraries will facilitate the identification of enhanced individual compounds. Individual compounds identified from such libraries may have increased specificity and decreased side effects early in the discovery phase. Testing began by screening ten diverse scaffolds as single mixtures (ranging from 17 340 to 4 879 681 compounds) for analgesia directly in the mouse tail withdrawal model. The “all X” mixture representing the library TPI-1954 was found to produce significant antinociception and lacked respiratory depression and hyperlocomotor effects using the Comprehensive Laboratory Animal Monitoring System (CLAMS). The TPI-1954 library is a pyrrolidine bis-piperazine and totals 738 192 compounds. This library has 26 functionalities at the first three positions of diversity made up of 28 392 compounds each (26 × 26 × 42) and 42 functionalities at the fourth made up of 19 915 compounds each (26 × 26 × 26). The 120 resulting mixtures representing each of the variable four positions were screened directly in vivo in the mouse 55 °C warm-water tail-withdrawal assay (ip administration). The 120 samples were then ranked in terms of their antinociceptive activity. The synthesis of 54 individual compounds was then carried out. Nine of the individual compounds produced dose-dependent antinociception equivalent to morphine. In practical terms what this means is that one would not expect multiexponential increases in activity as we move from the all-X mixture, to the positional scanning libraries, to the individual compounds. Actually because of the systematic formatting one would typically anticipate steady increases in activity as the complexity of the mixtures is reduced. This is in fact what we see in the current study. One of the final individual compounds identified, TPI 2213-17, lacked significant respiratory depression, locomotor impairment, or sedation. Our results represent an example of this unique approach for screening large mixture-based libraries directly in vivo to rapidly identify individual compounds

Bridging immunogenetics and immunoproteomics: Model positional scanning library analysis for Major Histocompatibility Complex class II DQ in Tursiops truncatus.

The Major Histocompatibility Complex (MHC) is a critical element in mounting an effective immune response in vertebrates against invading pathogens. Studies of MHC in wildlife populations have typically focused on assessing diversity within the peptide binding regions (PBR) of the MHC class II (MHC II) family, especially the DQ receptor genes. Such metrics of diversity, however, are of limited use to health risk assessment since functional analyses (where changes in the PBR are correlated to recognition/pathologies of known pathogen proteins), are difficult to conduct in wildlife species. Here we describe a means to predict the binding preferences of MHC proteins: We have developed a model positional scanning library analysis (MPSLA) by harnessing the power of mixture based combinatorial libraries to probe the peptide landscapes of distinct MHC II DQ proteins. The algorithm provided by NNAlign was employed to predict the binding affinities of sets of peptides generated for DQ proteins. These binding affinities were then used to retroactively construct a model Positional Scanning Library screen. To test the utility of the approach, a model screen was compared to physical combinatorial screens for human MHC II DP. Model library screens were generated for DQ proteins derived from sequence data from bottlenose dolphins from the Indian River Lagoon (IRL) and the Atlantic coast of Florida, and compared to screens of DQ proteins from Genbank for dolphin and three other cetaceans. To explore the peptide binding landscape for DQ proteins from the IRL, combinations of the amino acids identified as active were compiled into peptide sequence lists that were used to mine databases for representation in known proteins. The frequency of which peptide sequences predicted to bind the MHC protein are found in proteins from pathogens associated with marine mammals was found to be significant (p values <0.0001). Through this analysis, genetic variation in MHC (classes I and II) can now be associated with the binding repertoires of the expressed MHC proteins and subsequently used to identify target pathogens. This approach may be eventually applied to evaluate individual population and species risk for outbreaks of emerging diseases

Use and Implications of the Harmonic Mean Model on Mixtures for Basic Research and Drug Discovery

The use of the harmonic mean model for predicting the activities of a given mixture and its constituents has not previously been explored in the context of combinatorial libraries and drug discovery. Herein, the analyses of historical data confirm the harmonic mean as an accurate predictor of mixture activity. The implications of these results are discussed

Use and Implications of the Harmonic Mean Model on Mixtures for Basic Research and Drug Discovery

The use of the harmonic mean model for predicting the activities of a given mixture and its constituents has not previously been explored in the context of combinatorial libraries and drug discovery. Herein, the analyses of historical data confirm the harmonic mean as an accurate predictor of mixture activity. The implications of these results are discussed

Model positional scanning library and ranking for MCH II DP.

<p>Model positional scanning library and ranking for MCH II DP.</p

Method for model positional scanning library analysis (MPSLA).

<p>Nine steps that take the researcher from genetic sequence data, through MHC binding analysis, to protein and pathogen prediction.</p

Predicted binding affinities of peptide sequences derived from proteins encoded by cetacean MHC II DQ alleles.

<p>Binding affinities of 7,634 peptide sequences predicted by <i>NNAlign</i> were compared by counting the number of peptides below 100, 500, 1000, 5000 and 10,000nM thresholds. The algorithm was supplied with a 7,647 amino acid sequence and the DQA and B protein sequences from cetaceans (killer whale, sperm whale, finless dolphin) obtained from Genbank and from bottlenose dolphins in the Indian River Lagoon (IRL) and adjacent Atlantic coast (ATL) (DQ1-1; Protein derived from <i>DQA 1*01 DQB 1*01</i> DQ1-8; Protein derived from <i>DQA 1*01 DQB 1*08</i> DQ1-10; Protein derived from <i>DQA 1*01 DQB 1*10</i> and DQ2-4; Protein derived from <i>DQA 1*02 DQB 1*04</i>).</p

Comparison of rankings for dolphin (standard) to four IRL dolphins and 3 other cetaceans.

<p>Correlations were performed on scatterplots of amino acid ranking obtained from our standard DQ protein of Bottlenose dolphin (<i>Tursiops truncatus</i>), with each of the four proteins found in the IRL (DQ1-1, DQA 1–8, DQA1-10 and DQ2-4). Correlations were also performed comparing the amino acid ranking from DQ proteins for standard bottlenose dolphin to those of killer whale (<i>Orcinus orca</i>), Yangtze finless porpoise (<i>Neophocaena phocaenoides</i>) and sperm whale (<i>Physeter microcephalus</i>). Coefficients of determination (r²) derived from Pearson coefficients (r) are plotted for each of the 9 positions of the MPSLs.</p

Proteins and pathogens identified by MPSLA for dolphins from the IRL.

<p>Proteins and pathogens identified by MPSLA for dolphins from the IRL.</p

Summary of hits from search of UniProtKB database (90,645,980 million entries).

<p>Summary of hits from search of UniProtKB database (90,645,980 million entries).</p