Nikolaus Stiefl

Structural resemblances and comparisons of the relative pharmacological properties of imatinib and nilotinib

Although orphan drug applications required by the EMEA must include assessments of similarity to pre-existing products, these can be difficult to quantify. Here we illustrate a paradigm in comparing nilotinib to the prototype kinase inhibitor imatinib, and equate the degree of structural similarity to differences in properties. Nilotinib was discovered following re-engineering of imatinib, employing structural biology and medicinal chemistry strategies to optimise cellular potency and selectivity towards BCR-ABL1. Through evolving only to conserve these properties, this resulted in significant structural differences between nilotinib and imatinib, quantified by a Daylight-fingerprint-Tanimoto similarity coefficient of 0.6, with the meaning of this absolute measure being supported by an analysis of similarity distributions of similar drug-like molecules. This dissimilarity is reflected in the drugs having substantially different preclinical pharmacology and a lack of cross-intolerance in CML patients, which translates into nilotinib being an efficacious treatment for CML, with a favourable side-effect profile.

Evaluation of machine-learning methods for ligand-based virtual screening

Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed.

Validation tools for variable subset regression.

Variable selection is applied frequently in QSAR research. Since the selection process influences the characteristics of the finally chosen model, thorough validation of the selection technique is very important. Here, a validation protocol is presented briefly and two of the tools which are part of this protocol are introduced in more detail. The first tool, which is based on permutation testing, allows to assess the inflation of internal figures of merit (such as the cross-validated prediction error). The other tool, based on noise addition, can be used to determine the complexity and with it the stability of models generated by variable selection. The obtained statistical information is important in deciding whether or not to trust the predictive abilities of a specific model. The graphical output of the validation tools is easily accessible and provides a reliable impression of model performance. Among others, the tools were employed to study the influence of leave-one-out and leave-multiple-out cross-validation on model characteristics. Here, it was confirmed that leave-multiple-out cross-validation yields more stable models. To study the performance of the entire validation protocol, it was applied to eight different QSAR data sets with default settings. In all cases internal and external model performance was good, indicating that the protocol serves its purpose quite well.

Aziridide-based inhibitors of cathepsin L: synthesis, inhibition activity, and docking studies.

A comprehensive screening of N‐acylated aziridine (aziridide) based cysteine protease inhibitors containing either Boc‐Leu‐Caa (Caa=cyclic amino acid), Boc‐Gly‐Caa, or Boc‐Phe‐Ala attached to the aziridine nitrogen atom revealed Boc‐(S)‐Leu‐(S)‐Azy‐(S,S)‐Azi(OBn)2 (18 a) as a highly potent cathepsin L (CL) inhibitor (Ki=13 nM) (Azy=aziridine‐2‐carboxylate, Azi=aziridine‐2,3‐dicarboxylate). Docking studies, which also accounted for the unusual bonding situations (the flexibility and hybridization of the aziridides) predict that the inhibitor adopts a Y shape and spans across the entire active site cleft, binding into both the nonprimed and primed sites of CL.

Screening of electrophilic compounds yields an aziridinyl peptide as new active-site directed SARS-CoV main protease inhibitor

Abstract The coronavirus main protease, Mpro, is considered a major target for drugs suitable to combat coronavirus infections including the severe acute respiratory syndrome (SARS). In this study, comprehensive HPLC- and FRET-substrate-based screenings of various electrophilic compounds were performed to identify potential Mpro inhibitors. The data revealed that the coronaviral main protease is inhibited by aziridine- and oxirane-2-carboxylates. Among the trans-configured aziridine-2,3-dicarboxylates the Gly-Gly-containing peptide 2c was found to be the most potent inhibitor.

Evaluation of extended parameter sets for the 3D-QSAR technique MaP: implications for interpretability and model quality exemplified by antimalarially active naphthylisoquinoline alkaloids.

The 3D-QSAR technique MaP (Mapping Property distributions of molecular surfaces) characterises biologically active compounds in terms of the distribution of their surface properties (H-bond donor, H-bond acceptor, hydrophilic, weakly hydrophobic, strongly hydrophobic). The MaP descriptor is alignment-independent and yields chemically intuitive models. In this study, the impact of different operational parameters on the interpretability and model quality was investigated. Based on a set of antimalarially active naphtylisoquinoline alkaloids the effect of hydrophobicity assignment as well as the differentiation of H-bond propensity was evaluated according to a full factorial design. It turns out, that including different categories for H-bond donor strength significantly improved interpretability, reduced model complexity, and made possible the derivation of a novel pharmacophore hypothesis for this dataset. Further analysis of the factorial design reveals, that MaP models are robust to parameter changes and generate consistent models for different parameter settings.

Bringing the MMFF force field to the RDKit: implementation and validation

A general purpose force field such as MMFF94/MMFF94s, which can properly deal with a wide range of diverse structures, is very valuable in the context of a cheminformatics toolkit. Herein we present an open-source implementation of this force field within the RDKit. The new MMFF functionality can be accessed through a C++/C#/Python/Java application programming interface (API) developed along the lines of the one already available for UFF in the RDKit. Our implementation was fully validated against the official validation suite provided by the MMFF authors. All energies and gradients were correctly computed; moreover, atom type and force constants were correctly assigned for 3D molecules built from SMILES strings. To provide full flexibility, the available API provides direct access to include/exclude individual terms from the MMFF energy expression and to carry out constrained geometry optimizations. The availability of a MMFF-capable molecular mechanics engine coupled with the rest of the RDKit functionality and covered by the BSD license is appealing to researchers operating in both academia and industry.

FOCUS — Development of a Global Communication and Modeling Platform for Applied and Computational Medicinal Chemists

Communication of data and ideas within a medicinal chemistry project on a global as well as local level is a crucial aspect in the drug design cycle. Over a time frame of eight years, we built and optimized FOCUS, a platform to produce, visualize, and share information on various aspects of a drug discovery project such as cheminformatics, data analysis, structural information, and design. FOCUS is tightly integrated with internal services that involve-among others-data retrieval systems and in-silico models and provides easy access to automated modeling procedures such as pharmacophore searches, R-group analysis, and similarity searches. In addition, an interactive 3D editor was developed to assist users in the generation and docking of close analogues of a known lead. In this paper, we will specifically concentrate on issues we faced during development, deployment, and maintenance of the software and how we continually adapted the software in order to improve usability. We will provide usage examples to highlight the functionality as well as limitations of FOCUS at the various stages of the development process. We aim to make the discussion as independent of the software platform as possible, so that our experiences can be of more general value to the drug discovery community.

Making sure there's a "give" associated with the "take": producing and using open-source software in big pharma

In contrast to bioinformatics, open-source software is not as widely used in the pharmaceutical industry for molecular modeling and cheminformatics. Typical reasons given for this include problems with code quality, stability, and long-term support for the software (somehow this is less of a concern with bioinformatics software... kind of makes one think). Recently, our group has started making heavy use of an open-source cheminformatics toolkit RDKit [1] in our production environment. Importantly, we are not just acting as consumers of open-source software -- we are active members of the open-source community and have support from management to contribute code back to the project. In this presentation we will provide a brief overview of the RDKit itself and then present a number of case studies of how we have made use of this open-source platform. Examples will include using the toolkit for method development [2,3], integration with proprietary tools, and some recent (and upcoming) contributions to the open-souce community, including a database cartridge for fast and flexible similarity searching in the open-source PostgreSQL database [4], and adding support for the RDKit within the open-source pipelining platform Knime [5]. We will finish with a discussion of some practical aspects of working on and with open-source tools in a large research organization.

Structure-based validation of the 3D-QSAR technique MaP.

For three target proteins with different binding pocket characteristics (size and shape, hydrophobicity, hydrogen-bonding) a structure-based validation of the translationally and rotationally invariant 3D-QSAR technique MaP is performed (MaP: Mapping Property distributions of molecular surfaces). The structure-based validation procedure comprises two steps: first, QSAR models are derived without using the information of the target protein. Second, the models are back-projected into the crystal structure of the binding pockets and interpreted. It is demonstrated that MaP is able to identify characteristics important for ligand binding in the cases studied here. Moreover, it is demonstrated that MaP is a versatile 3D-QSAR technique since good, predictive models could be obtained for all three data sets showing distinct characteristics.