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GPCR structure, function, drug discovery and crystallography: report from Academia-Industry International Conference (UK Royal Society) Chicheley Hall, 1-2 September 2014.

Heifetz A, Schertler GF, Seifert R, Tate CG, Sexton PM, Gurevich VV, Fourmy D, Cherezov V, Marshall FH, Storer RI, Moraes I, Tikhonova IG, Tautermann CS, Hunt P, Ceska T, Hodgson S, Bodkin MJ, Singh S, Law RJ, Biggin PC - Naunyn Schmiedebergs Arch. Pharmacol. (2015)

Bottom Line: Secondly, the concept of biased signalling or functional selectivity is likely to be prevalent in many GPCRs, and this presents exciting new opportunities for selectivity and the control of side effects, especially when combined with increasing data regarding allosteric modulation.Subtle effects within the packing of the transmembrane helices are likely to mask and contribute to this aspect, which may play a role in species dependent behaviour.This is particularly important because it has ramifications for how we interpret pre-clinical data.

View Article: PubMed Central - PubMed

Affiliation: Evotec (UK) Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK, Alexander.Heifetz@Evotec.com.

ABSTRACT
G-protein coupled receptors (GPCRs) are the targets of over half of all prescribed drugs today. The UniProt database has records for about 800 proteins classified as GPCRs, but drugs have only been developed against 50 of these. Thus, there is huge potential in terms of the number of targets for new therapies to be designed. Several breakthroughs in GPCRs biased pharmacology, structural biology, modelling and scoring have resulted in a resurgence of interest in GPCRs as drug targets. Therefore, an international conference, sponsored by the Royal Society, with world-renowned researchers from industry and academia was recently held to discuss recent progress and highlight key areas of future research needed to accelerate GPCR drug discovery. Several key points emerged. Firstly, structures for all three major classes of GPCRs have now been solved and there is increasing coverage across the GPCR phylogenetic tree. This is likely to be substantially enhanced with data from x-ray free electron sources as they move beyond proof of concept. Secondly, the concept of biased signalling or functional selectivity is likely to be prevalent in many GPCRs, and this presents exciting new opportunities for selectivity and the control of side effects, especially when combined with increasing data regarding allosteric modulation. Thirdly, there will almost certainly be some GPCRs that will remain difficult targets because they exhibit complex ligand dependencies and have many metastable states rendering them difficult to resolve by crystallographic methods. Subtle effects within the packing of the transmembrane helices are likely to mask and contribute to this aspect, which may play a role in species dependent behaviour. This is particularly important because it has ramifications for how we interpret pre-clinical data. In summary, collaborative efforts between industry and academia have delivered significant progress in terms of structure and understanding of GPCRs and will be essential for resolving problems associated with the more difficult targets in the future.

No MeSH data available.


Automated multi-objective compound design using reaction vectors (26K Reaction Db and 93K Reagents) starting from piperidine and using four objectives: similarity to haloperidol and Ziprasidone pharmacophores, Dopamine D2, α1B Adrenergic and Histamine QSAR models. The tri-cyclics generated appeared similar to known anti-pyschotics, Chlorpromazine and Fluphenazine
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Fig15: Automated multi-objective compound design using reaction vectors (26K Reaction Db and 93K Reagents) starting from piperidine and using four objectives: similarity to haloperidol and Ziprasidone pharmacophores, Dopamine D2, α1B Adrenergic and Histamine QSAR models. The tri-cyclics generated appeared similar to known anti-pyschotics, Chlorpromazine and Fluphenazine

Mentions: The polypharmacology associated with current typical and atypical anti-psychotics is complex, and as an example, the question of how do we go about designing a novel anti-psychotic given the tools and data we have access to today was raised. The opensource ChEMBL space polypharmacology network viewer (Fechner et al. 2013) was introduced as an interactive way to review the rich pharmacology accessible in the ChEMBL database and identify some good starting points for drug design. The experimental polypharmacology associated with the hits can be complemented using target prediction ligand similarity-based approaches such as the similarity ensemble approach (SEA; Keiser et al. 2009) or broad panel-based predictive modelling approaches (Ghosh and Jones 2014). Predictive modelling approaches were also used to build protein target QSARs that in combination with pharmacophore triplet compound similarity were used to develop a multi-objective scoring function. Given a small organic fragment, an automated evolutionary design algorithm using reaction vectors was used to grow a molecule by simultaneously optimising the multi-parameters required for the targeted phenotype polypharmacology (Patel et al. 2009) (see Fig. 15). The reaction vector design approach was extended to whole reaction sequences and ultimately reaction networks. A GPR38 reaction network was built which exemplified that the chemistry phase space around a hit could be readily expanded to that of closely accessible molecules. This would enable better sampling and rapid medchem design.Fig. 15


GPCR structure, function, drug discovery and crystallography: report from Academia-Industry International Conference (UK Royal Society) Chicheley Hall, 1-2 September 2014.

Heifetz A, Schertler GF, Seifert R, Tate CG, Sexton PM, Gurevich VV, Fourmy D, Cherezov V, Marshall FH, Storer RI, Moraes I, Tikhonova IG, Tautermann CS, Hunt P, Ceska T, Hodgson S, Bodkin MJ, Singh S, Law RJ, Biggin PC - Naunyn Schmiedebergs Arch. Pharmacol. (2015)

Automated multi-objective compound design using reaction vectors (26K Reaction Db and 93K Reagents) starting from piperidine and using four objectives: similarity to haloperidol and Ziprasidone pharmacophores, Dopamine D2, α1B Adrenergic and Histamine QSAR models. The tri-cyclics generated appeared similar to known anti-pyschotics, Chlorpromazine and Fluphenazine
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4495723&req=5

Fig15: Automated multi-objective compound design using reaction vectors (26K Reaction Db and 93K Reagents) starting from piperidine and using four objectives: similarity to haloperidol and Ziprasidone pharmacophores, Dopamine D2, α1B Adrenergic and Histamine QSAR models. The tri-cyclics generated appeared similar to known anti-pyschotics, Chlorpromazine and Fluphenazine
Mentions: The polypharmacology associated with current typical and atypical anti-psychotics is complex, and as an example, the question of how do we go about designing a novel anti-psychotic given the tools and data we have access to today was raised. The opensource ChEMBL space polypharmacology network viewer (Fechner et al. 2013) was introduced as an interactive way to review the rich pharmacology accessible in the ChEMBL database and identify some good starting points for drug design. The experimental polypharmacology associated with the hits can be complemented using target prediction ligand similarity-based approaches such as the similarity ensemble approach (SEA; Keiser et al. 2009) or broad panel-based predictive modelling approaches (Ghosh and Jones 2014). Predictive modelling approaches were also used to build protein target QSARs that in combination with pharmacophore triplet compound similarity were used to develop a multi-objective scoring function. Given a small organic fragment, an automated evolutionary design algorithm using reaction vectors was used to grow a molecule by simultaneously optimising the multi-parameters required for the targeted phenotype polypharmacology (Patel et al. 2009) (see Fig. 15). The reaction vector design approach was extended to whole reaction sequences and ultimately reaction networks. A GPR38 reaction network was built which exemplified that the chemistry phase space around a hit could be readily expanded to that of closely accessible molecules. This would enable better sampling and rapid medchem design.Fig. 15

Bottom Line: Secondly, the concept of biased signalling or functional selectivity is likely to be prevalent in many GPCRs, and this presents exciting new opportunities for selectivity and the control of side effects, especially when combined with increasing data regarding allosteric modulation.Subtle effects within the packing of the transmembrane helices are likely to mask and contribute to this aspect, which may play a role in species dependent behaviour.This is particularly important because it has ramifications for how we interpret pre-clinical data.

View Article: PubMed Central - PubMed

Affiliation: Evotec (UK) Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK, Alexander.Heifetz@Evotec.com.

ABSTRACT
G-protein coupled receptors (GPCRs) are the targets of over half of all prescribed drugs today. The UniProt database has records for about 800 proteins classified as GPCRs, but drugs have only been developed against 50 of these. Thus, there is huge potential in terms of the number of targets for new therapies to be designed. Several breakthroughs in GPCRs biased pharmacology, structural biology, modelling and scoring have resulted in a resurgence of interest in GPCRs as drug targets. Therefore, an international conference, sponsored by the Royal Society, with world-renowned researchers from industry and academia was recently held to discuss recent progress and highlight key areas of future research needed to accelerate GPCR drug discovery. Several key points emerged. Firstly, structures for all three major classes of GPCRs have now been solved and there is increasing coverage across the GPCR phylogenetic tree. This is likely to be substantially enhanced with data from x-ray free electron sources as they move beyond proof of concept. Secondly, the concept of biased signalling or functional selectivity is likely to be prevalent in many GPCRs, and this presents exciting new opportunities for selectivity and the control of side effects, especially when combined with increasing data regarding allosteric modulation. Thirdly, there will almost certainly be some GPCRs that will remain difficult targets because they exhibit complex ligand dependencies and have many metastable states rendering them difficult to resolve by crystallographic methods. Subtle effects within the packing of the transmembrane helices are likely to mask and contribute to this aspect, which may play a role in species dependent behaviour. This is particularly important because it has ramifications for how we interpret pre-clinical data. In summary, collaborative efforts between industry and academia have delivered significant progress in terms of structure and understanding of GPCRs and will be essential for resolving problems associated with the more difficult targets in the future.

No MeSH data available.