Enabling drug discovery project decisions with integrated computational chemistry and informatics

Computational chemistry/informatics scientists and software engineers in Genentech Small Molecule Drug Discovery collaborate with experimental scientists in a therapeutic project-centric environment. Our mission is to enable and improve pre-clinical drug discovery design and decisions. Our goal is to deliver timely data, analysis, and modeling to our therapeutic project teams using best-in-class software tools. We describe our strategy, the organization of our group, and our approaches to reach this goal. We conclude with a summary of the interdisciplinary skills required for computational scientists and recommendations for their training.     

Identifying promising compounds in drug discovery: genetic algorithms and some new statistical techniques

Throughout the drug discovery process, discovery teams are compelled to use statistics for making decisions using data from a variety of inputs. For instance, teams are asked to prioritize compounds for subsequent stages of the drug discovery process, given results from multiple screens. To assist in the prioritization process, we propose a desirability function to account for a priori scientific knowledge; compounds can then be prioritized based on their desirability scores. In addition to identifying existing desirable compounds, teams often use prior knowledge to suggest new, potentially promising compounds to be created in the laboratory. Because the chemistry space to search can be dauntingly large, we propose the sequential elimination of level combinations (SELC) method for identifying new optimal compounds. We illustrate this method on a combinatorial chemistry example.     

DEGAS: sharing and tracking target compound ideas with external collaborators

To minimize the risk of failure in clinical trials, drug discovery teams must propose active and selective clinical candidates with good physicochemical properties. An additional challenge is that today drug discovery is often conducted by teams at different geographical locations. To improve the collaborative decision making on which compounds to synthesize, we have implemented DEGAS, an application which enables scientists from Genentech and from collaborating external partners to instantly access the same data. DEGAS was implemented to ensure that only the best target compounds are made and that they are made without duplicate effort. Physicochemical properties and DMPK model predictions are computed for each compound to allow the team to make informed decisions when prioritizing. The synthesis progress can be easily tracked. While developing DEGAS, ease of use was a particular goal in order to minimize the difficulty of training and supporting remote users.     

Applications of high throughput microsomal stability assay in drug discovery

High throughput in vitro microsomal stability assays are widely used in drug discovery as an indicator for in vivo stability, which affects pharmacokinetics. This is based on in-depth research involving a limited number of model drug-like compounds that are cleared predominantly by cytochrome P450 metabolism. However, drug discovery compounds are often not drug-like, are assessed with high throughput assays, and have many potential uncharacterized in vivo clearance mechanisms. Therefore, it is important to determine the correlation between high throughput in vitro microsomal stability data and abbreviated discovery in vivo pharmacokinetics study data for a set of drug discovery compounds in order to have evidence for how the in vitro assay can be reliably applied by discovery teams for making critical decisions. In this study the relationship between in vitro single time point high throughput microsomal stability and in vivo clearance from abbreviated drug discovery pharmacokinetics studies was examined using 306 real world drug discovery compounds. The results showed that in vitro Phase I microsomal stability t(1/2) is significantly correlated to in vivo clearance with a p-value<0.001. For compounds with low in vitro rat microsomal stability (t(1/2)<15 min), 87% showed high clearance in vivo (CL>25 mL/min/kg). This demonstrates that high throughput microsomal stability data are very effective in identifying compounds with significant clearance liabilities in vivo. For compounds with high in vitro rat microsomal stability (t(1/2)>15 min), no significant differentiation was observed between high and low clearance compounds. This is likely owing to other clearance pathways, in addition to cytochrome P450 metabolism that enhances in vivo clearance. This finding supports the strategy used by medicinal chemists and drug discovery teams of applying the in vitro data to triage compounds for in vivo PK and efficacy studies and guide structural modification to improve metabolic stability. When in vitro and in vivo data are both available for a compound, potential in vivo clearance pathways can be diagnosed to guide further discovery studies.     

CADD medicine: design is the potion that can cure my disease

The acronym “CADD” is often used interchangeably to refer to “Computer Aided Drug Discovery” and “Computer Aided Drug Design”. While the former definition implies the use of a computer to impact one or more aspects of discovering a drug, in this paper we contend that computational chemists are most effective when they enable teams to apply true design principles as they strive to create medicines to treat human disease. We argue that teams must bring to bear multiple sub-disciplines of computational chemistry in an integrated manner in order to utilize these principles to address the multi-objective nature of the drug discovery problem. Impact, resourcing principles, and future directions for the field are also discussed, including areas of future opportunity as well as a cautionary note about hype and hubris.     

A searching and reporting system for relational databases using a graph-based metadata representation

Relational databases are the current standard for storing and retrieving data in the pharmaceutical and biotech industries. However, retrieving data from a relational database requires specialized knowledge of the database schema and of the SQL query language. At Anadys, we have developed an easy-to-use system for searching and reporting data in a relational database to support our drug discovery project teams. This system is fast and flexible and allows users to access all data without having to write SQL queries. This paper presents the hierarchical, graph-based metadata representation and SQL-construction methods that, together, are the basis of this system's capabilities.     

Distant collaboration in drug discovery: the LINK3D project

The work describes the development of novel software supporting synchronous distant collaboration between scientists involved in drug discovery and development projects. The program allows to visualize and share data as well as to interact in real time using standard intranets and Internet resources. Direct visualization of 2D and 3D molecular structures is supported and original tools for facilitating remote discussion have been integrated. The software is multiplatform (MS-Windows, SGI-IRIX, Linux), allowing for a seamless integration of heterogeneous working environments. The project aims to support collaboration both within and between academic and industrial institutions. Since confidentiality is very important in some scenarios, special attention has been paid to security aspects. The article presents the research carried out to gather the requirements of collaborative software in the field of drug discovery and development and describes the features of the first fully functional prototype obtained. Real-world testing activities carried out on this prototype in order to guarantee its adequacy in diverse environments are also described and discussed.In addition to the mentioned institutions the LINK3D Consortium is constituted by     

Modeling &amp; Informatics at Vertex Pharmaceuticals Incorporated: our philosophy for sustained impact

Molecular modelers and informaticians have the unique opportunity to integrate cross-functional data using a myriad of tools, methods and visuals to generate information. Using their drug discovery expertise, information is transformed to knowledge that impacts drug discovery. These insights are often times formulated locally and then applied more broadly, which influence the discovery of new medicines. This is particularly true in an organization where the members are exposed to projects throughout an organization, such as in the case of the global Modeling & Informatics group at Vertex Pharmaceuticals. From its inception, Vertex has been a leader in the development and use of computational methods for drug discovery. In this paper, we describe the Modeling & Informatics group at Vertex and the underlying philosophy, which has driven this team to sustain impact on the discovery of first-in-class transformative medicines.     

Front Cover: Katritzky Salts for the Synthesis of Unnatural Amino Acids and Late-Stage Functionalization of Peptides (Eur. J. Org. Chem. 12/2023)

Peptide drug discovery often benefits from the large structural diversity permitted by unnatural amino acids (UAAs). Indeed, numerous approved peptide drugs include UAAs in their sequences. Therefore, innovative chemical approaches either to synthesize UAAs or to allow late-stage functionalization of peptides are emerging themes in peptide drug discovery. Thanks to the recent advances in deaminative strategies using alkylpyridiniums salts, often referred to as Katritzky salts, a variety of radical alkylation methods have been developed. In recent years the use of Katritzky salts have become popular in peptide chemistry due to their ease of preparation from a primary amine, which is a predominant functional group in amino acids. This review highlights the progress that has been made by using Katritzky salts in the synthesis of UAAs, late-stage peptide functionalization, and peptide macrocyclization.     

Advanced biological and chemical discovery (ABCD): centralizing discovery knowledge in an inherently decentralized world

We present ABCD, an integrated drug discovery informatics platform developed at Johnson & Johnson Pharmaceutical Research & Development, L.L.C. ABCD is an attempt to bridge multiple continents, data systems, and cultures using modern information technology and to provide scientists with tools that allow them to analyze multifactorial SAR and make informed, data-driven decisions. The system consists of three major components: (1) a data warehouse, which combines data from multiple chemical and pharmacological transactional databases, designed for supreme query performance; (2) a state-of-the-art application suite, which facilitates data upload, retrieval, mining, and reporting, and (3) a workspace, which facilitates collaboration and data sharing by allowing users to share queries, templates, results, and reports across project teams, campuses, and other organizational units. Chemical intelligence, performance, and analytical sophistication lie at the heart of the new system, which was developed entirely in-house. ABCD is used routinely by more than 1000 scientists around the world and is rapidly expanding into other functional areas within the J&J organization.     

Looking Beyond the Column: An Investigation into Method Irreproducibility for an Active Pharmaceutical Ingredient

Irreproducibility during initial development of a high performance liquid chromatography method is seldom investigated in a thorough and careful manner when working in an industrial setting. For a drug project in the early developmental stages, the LC method is often changed, sometimes drastically, if one encounters irreproducibility in the method. Too often a method is deemed irreproducible due to column lot variability, diluent effects, or pH effects with little or no experiments taking place for justification and little thought given to why the irreproducibility may have occurred in the first case. In this paper, a case study is presented in which a systematic approach was carried out in order to determine the exact reason why method irreproducibility was occurring. The findings of the investigation were then drawn onto change a method with poor reproducibility into one that is rugged without the need to start from the beginning and develop a new method.     

Sulfoximines: A Neglected Opportunity in Medicinal Chemistry

Innovation has frequently been described as the key to drug discovery. However, in the daily routine, medicinal chemists often tend to stick to the functional groups and structural elements they know and love. Blockbuster cancer drug Velcade (bortezomib), for example, was rejected by more than 50 companies, supposedly because of its unusual boronic acid function (as often repeated: “only a moron would put boron in a drug!”). Similarly, in the discovery process of the pan‐CDK inhibitor BAY 1000394, the unconventional proposal to introduce a sulfoximine group into the lead series also led to sneers and raised eyebrows, since sulfoximines have seldom been used in medicinal chemistry. However, it was the introduction of the sulfoximine group that finally allowed the fundamental issues of the project to be overcome, culminating in the identification of the clinical sulfoximine pan‐CDK inhibitor BAY 1000394. This Minireview provides an overview of a widely neglected opportunity in medicinal chemistry—the sulfoximine group.     

Modern drug discovery technologies: opportunities and challenges in lead discovery

The identification of promising hits and the generation of high quality leads are crucial steps in the early stages of drug discovery projects. The definition and assessment of both chemical and biological space have revitalized the screening process model and emphasized the importance of exploring the intrinsic complementary nature of classical and modern methods in drug research. In this context, the widespread use of combinatorial chemistry and sophisticated screening methods for the discovery of lead compounds has created a large demand for small organic molecules that act on specific drug targets. Modern drug discovery involves the employment of a wide variety of technologies and expertise in multidisciplinary research teams. The synergistic effects between experimental and computational approaches on the selection and optimization of bioactive compounds emphasize the importance of the integration of advanced technologies in drug discovery programs. These technologies (VS, HTS, SBDD, LBDD, QSAR, and so on) are complementary in the sense that they have mutual goals, thereby the combination of both empirical and in silico efforts is feasible at many different levels of lead optimization and new chemical entity (NCE) discovery. This paper provides a brief perspective on the evolution and use of key drug design technologies, highlighting opportunities and challenges.     

Achieving maximum ROI from corporate databases: exploiting your databases with integrated querying for better decision-making

In order to increase the rate of drug discovery, pharmaceutical and biotechnology companies spend billions of dollars a year assembling research databases. Current trends still indicate a falling rate in the discovery of New Molecular Entities (NMEs). It is widely accepted that the data need to be integrated in order for it to add value. The degree to which this must be achieved is often misunderstood. The true goal of data integration must be to provide accessible knowledge. If knowledge cannot be gained from these data, then it will invalidate the business case for gathering it. Current data integration solutions focus on the initial task of integrating the actual data and to some extent, also address the need to allow users to access integrated information. Typically the search tools that are provided are either restrictive forms or free text based. While useful, neither of these solutions is suitable for providing full coverage of large numbers of integrated structured data sources. One solution to this accessibility problem is to present the integrated data in a collated manner that allows users to browse and explore it and also perform complex ad-hoc searches on it within a scientific context and without the need for advanced Information Technology (IT) skills. Additionally, the solution should be maintainable by 'in-house' administrators rather than requiring expensive consultancy. This paper examines the background to this problem, investigates the requirements for effective exploitation of corporate data and presents a novel effective solution.     

Looking Beyond the Column: An Investigation into Method Irreproducibility for an Active Pharmaceutical Ingredient

Irreproducibility during initial development of a high performance liquid chromatography method is seldom investigated in a thorough and careful manner when working in an industrial setting. For a drug project in the early developmental stages, the LC method is often changed, sometimes drastically, if one encounters irreproducibility in the method. Too often a method is deemed irreproducible due to column lot variability, diluent effects, or pH effects with little or no experiments taking place for justification and little thought given to why the irreproducibility may have occurred in the first case. In this paper, a case study is presented in which a systematic approach was carried out in order to determine the exact reason why method irreproducibility was occurring. The findings of the investigation were then drawn onto change a method with poor reproducibility into one that is rugged without the need to start from the beginning and develop a new method.

     

Composite multi-parameter ranking of real and virtual compounds for design of MC4R agonists: Renaissance of the Free-Wilson methodology

Drug design is a multi-parameter task present in the analysis of experimental data for synthesized compounds and in the prediction of new compounds with desired properties. This article describes the implementation of a binned scoring and composite ranking scheme for 11 experimental parameters that were identified as key drivers in the MC4R project. The composite ranking scheme was implemented in an AstraZeneca tool for analysis of project data, thereby providing an immediate re-ranking as new experimental data was added. The automated ranking also highlighted compounds overlooked by the project team. The successful implementation of a composite ranking on experimental data led to the development of an equivalent virtual score, which was based on Free-Wilson models of the parameters from the experimental ranking. The individual Free-Wilson models showed good to high predictive power with a correlation coefficient between 0.45 and 0.97 based on the external test set. The virtual ranking adds value to the selection of compounds for synthesis but error propagation must be controlled. The experimental ranking approach adds significant value, is parameter independent and can be tuned and applied to any drug discovery project.     

Reaction site mapping of xenobiotic biotransformations

Predictive metabolism methods can be used in drug discovery projects to enhance the understanding of structure-metabolism relationships. The present study uses data mining methods to exploit biotransformation data that have been recorded in the MDL Metabolite database. Reacting center fingerprints were derived from a comparison of substrates and their corresponding products listed in the database. This process yields two fingerprint databases: all atoms in all substrates and all reacting centers. The metabolic reaction data are then mined by submitting a new molecule and searching for fingerprint matches to every atom in the new molecule in both databases. An "occurrence ratio" is derived from the fingerprint matches between the submitted compound and the reacting center and substrate fingerprint databases. Normalization of the occurrence ratio within each submitted molecule enables the results of the search to be rank-ordered as a measure of the relative frequency of a reaction occurring at a specific site within the submitted molecule. Predictive performance that would allow this method to be used by drug discovery teams to generate useful hypotheses regarding structure metabolism relationships was observed.     

Using silico methods predicting ligands for orphan GPCRs

The G-protein coupled receptor (GPCR) superfamily is one of the most important drug target classes for the pharmaceutical industry. The completion of the human genome project has revealed that there are more than 300 potential GPCR targets of interest. The identification of their natural ligands can gain significant insights into regulatory mechanisms of cellular signaling networks and provide unprecedented opportunities for drug discovery. Much effort has been directed towards the GPCR ligand discovery study by both academic institutions and pharmaceutical industries. However, the endogenous ligands still remain unknown for about 150 GPCRs in the human genome. It is necessary to develop new strategies to predict candidate ligands for these so-called orphan receptors. Computational techniques are playing an increasingly important role in finding and validating novel ligands for orphan GPCRs (oGPCRs). In this paper, we focus on recent development in applying bioinformatics approaches for the discovery of GPCR ligands. In addition, some of the data resources for ligand identification are also provided.     

A probabilistic approach to classifying metabolic stability

Metabolic stability is an important property of drug molecules that should-optimally-be taken into account early on in the drug design process. Along with numerous medium- or high-throughput assays being implemented in early drug discovery, a prediction tool for this property could be of high value. However, metabolic stability is inherently difficult to predict, and no commercial tools are available for this purpose. In this work, we present a machine learning approach to predicting metabolic stability that is tailored to compounds from the drug development process at Bayer Schering Pharma. For four different in vitro assays, we develop Bayesian classification models to predict the probability of a compound being metabolically stable. The chosen approach implicitly takes the "domain of applicability" into account. The developed models were validated on recent project data at Bayer Schering Pharma, showing that the predictions are highly accurate and the domain of applicability is estimated correctly. Furthermore, we evaluate the modeling method on a set of publicly available data.