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Which compound to select in lead optimization? Prospectively validated proteochemometric models guide preclinical development.

ABSTRACT: In quite a few diseases, drug resistance due to target variability poses a serious problem in pharmacotherapy. This is certainly true for HIV, and hence, it is often unknown which drug is best to use or to develop against an individual HIV strain. In this work we applied 'proteochemometric' modeling of HIV Non-Nucleoside Reverse Transcriptase (NNRTI) inhibitors to support preclinical development by predicting compound performance on multiple mutants in the lead selection stage. Proteochemometric models are based on both small molecule and target properties and can thus capture multi-target activity relationships simultaneously, the targets in this case being a set of 14 HIV Reverse Transcriptase (RT) mutants. We validated our model by experimentally confirming model predictions for 317 untested compound-mutant pairs, with a prediction error comparable with assay variability (RMSE 0.62). Furthermore, dependent on the similarity of a new mutant to the training set, we could predict with high accuracy which compound will be most effective on a sequence with a previously unknown genotype. Hence, our models allow the evaluation of compound performance on untested sequences and the selection of the most promising leads for further preclinical research. The modeling concept is likely to be applicable also to other target families with genetic variability like other viruses or bacteria, or with similar orthologs like GPCRs.

SUBMITTER: van Westen GJ

PROVIDER: S-EPMC3223189 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Which compound to select in lead optimization? Prospectively validated proteochemometric models guide preclinical development.

van Westen Gerard J P GJ Wegner Jörg K JK Geluykens Peggy P Kwanten Leen L Vereycken Inge I Peeters Anik A Ijzerman Adriaan P AP van Vlijmen Herman W T HW Bender Andreas A

PloS one 20111123 11

In quite a few diseases, drug resistance due to target variability poses a serious problem in pharmacotherapy. This is certainly true for HIV, and hence, it is often unknown which drug is best to use or to develop against an individual HIV strain. In this work we applied 'proteochemometric' modeling of HIV Non-Nucleoside Reverse Transcriptase (NNRTI) inhibitors to support preclinical development by predicting compound performance on multiple mutants in the lead selection stage. Proteochemometric ...[more]

PMID: 22132107

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Project description:BackgroundThe fungal natural products illudin S and M have been investigated as precursors for the development of semisynthetic anticancer agents such as Irofulven (illudin S derivative) which is currently in phase II clinical trials. Recently, illudin M derivatives have shown improved in vitro selectivity towards cancer cells encouraging further investigation. This requires a stable supply of the precursor which is produced by Basidiomycota of the genus Omphalotus. We have recently reported a robust shake flask process for the production of gram quantities of illudin M from Omphalotus nidiformis aiming to transfer that process into stirred tank bioreactors, which can be used in a commercial production set-up. However, process transfer across different systems is not straightforward and particularly challenging when the producer is morphologically complex. There are only a few reports that address the development of bioprocesses for the production of compounds from Basidiomycota as these organisms have not been extensively studied because of their complex life cycles and often are difficult to cultivate under laboratory conditions.ResultsThe recently developed shake flask process delivering stable titers of ~ 940 mg L-1 of illudin M was investigated using off-gas analysis to identify critical parameters which facilitated the transfer from shaken into stirred tank bioreactors. Comparable titers to the shake flask process were achieved in 2 L stirred tank bioreactors (1.5 L working volume) by controlling growth of biomass with a carefully timed pH-shift combined with an improved precursor-feeding strategy. A scale-up experiment in a 15 L bioreactor (10 L working volume), resembling the process at 1.5 L resulted in 523 mg L-1 and is the starting point for optimization of the identified parameters at that scale.ConclusionBy identifying and controlling key process parameters, the production process for illudin M was transferred from shake flasks into 2 L stirred tank bioreactors reaching a comparable titer (> 900 mg L-1), which is significantly higher than any previously reported. The insights obtained from 10 L scale pave the way towards further scale-up studies that will enable a sustainable supply of illudin M to support preclinical and clinical development programs.

Project description:Infection with HIV cannot currently be cured; however it can be controlled by combination treatment with multiple anti-retroviral drugs. Given different viral genotypes for virtually each individual patient, the question now arises which drug combination to use to achieve effective treatment. With the availability of viral genotypic data and clinical phenotypic data, it has become possible to create computational models able to predict an optimal treatment regimen for an individual patient. Current models are based only on sequence data derived from viral genotyping; chemical similarity of drugs is not considered. To explore the added value of chemical similarity inclusion we applied proteochemometric models, combining chemical and protein target properties in a single bioactivity model. Our dataset was a large scale clinical database of genotypic and phenotypic information (in total ca. 300,000 drug-mutant bioactivity data points, 4 (NNRTI), 8 (NRTI) or 9 (PI) drugs, and 10,700 (NNRTI) 10,500 (NRTI) or 27,000 (PI) mutants). Our models achieved a prediction error below 0.5 Log Fold Change. Moreover, when directly compared with previously published sequence data, derived models PCM performed better in resistance classification and prediction of Log Fold Change (0.76 log units versus 0.91). Furthermore, we were able to successfully confirm both known and identify previously unpublished, resistance-conferring mutations of HIV Reverse Transcriptase (e.g. K102Y, T216M) and HIV Protease (e.g. Q18N, N88G) from our dataset. Finally, we applied our models prospectively to the public HIV resistance database from Stanford University obtaining a correct resistance prediction rate of 84% on the full set (compared to 80% in previous work on a high quality subset). We conclude that proteochemometric models are able to accurately predict the phenotypic resistance based on genotypic data even for novel mutants and mixtures. Furthermore, we add an applicability domain to the prediction, informing the user about the reliability of predictions.

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Which compound to select in lead optimization? Prospectively validated proteochemometric models guide preclinical development.

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Which compound to select in lead optimization? Prospectively validated proteochemometric models guide preclinical development.

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