A mapping based on physico-chemical features: lessons learned

Transcription

1 A mapping based on physico-chemical features: lessons learned Ann DETROYER, PhD L Oréal Research and Innovation, Aulnay, France

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3 EPA (ToxCast ) - L Oréal Partners to develop high throughput and non animal testing and assessment methods useful for evaluating sub-chronic and chronic non cancer risk to human health. Project just starting Possible deliverables: Predictive models (similar to the liver cancer model in Judson et al., 2010, and the reproductive toxicity model in Martin et al., 2011.) Weight of evidence models similar to those developed for endocrine disruption (Reif et al 2010), and hepatic cancer (Shah et al 2010). Databases supporting model development and validation of model performance.

4 EPA (ToxCast ) - L Oréal Q : Enrich the ToxCast selection list for development of a predictive model for systemic toxicity for real life chemicals? actives diverse dyes and pigments fatty substances surfactants A : A comparative study of the US EPA and L Oréal chemical spaces using a panel of 19 calculated structural and physico-chemical properties often used in structure activity models.

5 Data sets US EPA chemical space: 3422 structures including 929 structures ToxCast Phase 1&2 sets L Oréal chemical space: 708 structures Structure extraction from smiles code or name : Leadscope v "N2S" (Name to Structure) software v.12.0 Structure optimization and property calculations Adriana Code v Proprietary model developed using Pipeline Pilot v.8 platform Structural and physicochemical parameters considered description of the molecular size : Weight, NAtoms description of the molecular shape : Complexity, RComplexity, NRotBond, NStereo physico-chemical properties: HAcc, HAcc_N, HAcc_O, Hdon, HDon_N, HDon_O, TPSA, Polariz, Dipole, NViolationsRo5, NViolationsExtRo5, XLogP, LogS Principal Component Analysis : Matlab v.2011a

6 US-EPA v. L Oréal inventory L Oréal inventory US-EPA inventory High overlap of the L Oréal and US EPA inventory: no substantial difference between the two chemical spaces

7 CP 1 : 41.82% CP 2 : 23.64% Plan factoriel à 2 dimensions US-EPA(ToxCast Phase1) US-EPA(ToxCast Phase2) L Oréal inventory High overlap of the L Oréal inventory and the ToxCast Phase 1&2 sets Wider expansion of The L Oréal chemical space ToxCast 1 and 2 v. L Oréal inventory

8 ToxCast 1 and 2 v. L Oréal inventory Weig ht HDo n HDo n-n HDo n-o HAcc HAcc -N HAcc -O XLo gp TPS A Pol ariz Dip ole Log S NR ot Bond NVio lati onsro 5 NVio lati onsext Ro5 NAt oms NS ter eo Co mplexi ty RC ompl exit y CP 1 : % CP 2 : % Group 1 Group 2 Plot describes 65% of the variation in structural and physic-chemical parameter values within the two chemical spaces; this variation is mainly due to high values obtained for the parameters pertaining to Groups 1&2 US-EPA(ToxCast Phase1) US-EPA(ToxCast Phase2) L Oréal inventory

9 CP 1 : 41.82% CP 2 : 23.64% Plan factoriel à 2 dimensions A C B D ToxCast 1 and 2 v. L Oréal inventory The molecules commonly shared seem to have a parameter profile that fits with the Lipinski rules drug-like molecules (Cluster A) The molecules appearing outside of this correlation circle are : increasingly polar (Cluster B) increasingly bigger, more flexible and apolar (Cluster C) or the sum of both i.e. increasingly complex (Cluster D)

10 High overlap of the L Oréal inventory and the ToxCast Phase 1&2 sets (= drug-like molecules) Wider expansion of The L Oréal chemical space (= molecules increasingly extreme and/or complex in physchem properties) Knowledge and models derived from ToxCast Phase I&2 could be extended to a broader chemical space if our selected physico-chemical properties were to be useful when describing systemic toxicity Some of L Oréal s more diverse ingredients could be used in the development of mechanism-based models

11 EPA (ToxCast ) - L Oréal Identification of physico-chemical parameters that would best describe mixtures and polymers (not covered in this study) Elaboration of an action plan for the development of a predictive model for non-cancer systemic toxicity: Compilation of additional in vivo data (and population of ToxRefDB) Generation of toxicity profiles Identification of anchoring endpoints Selection of the ingredients for training and validation sets

12 Thank you for your attention! L Oréal R&I G. Ouédraogo S. Loisel-Joubert R. Note L. Bourouf H. Noçairi S. Ringeissen C. Chettaoui NCCT-US EPA A. Richard D. Reif D. Dix K. Crofton M. Martin R. Judson J.R. Meunier P. Berthe J. Cotovio