Comparison of logP and logD correction models trained with public and proprietary data sets.

In drug discovery, partition and distribution coefficients, logP and logD for octanol/water, are widely used as metrics of the lipophilicity of molecules, which in turn have a strong influence on the bioactivity and bioavailability of potential drugs. There are a variety of established methods, mostly fragment or atom-based, to calculate logP while logD prediction generally relies on calculated logP and pKa for the estimation of neutral and ionized populations at a given pH. Algorithms such as ClogP have limitations generally leading to systematic errors for chemically related molecules while pKa estimation is generally more difficult due to the interplay of electronic, inductive and conjugation effects for ionizable moieties. We propose an integrated machine learning QSAR modeling approach to predict logD by training the model with experimental data while using ClogP and pKa predicted by commercial software as model descriptors. By optimizing the loss function for the ClogD calculated by the software, we build a correction model that incorporates both descriptors from the software and available experimental logD data. Additionally, we calculate logP from the logD model using the software predicted pKa's. Here, we have trained models using publicly or commercial available logD data to show that this approach can improve on commercial software predictions of lipophilicity. When applied to other logD data sets, this approach extends the domain of applicability of logD and logP predictions over commercial software. Performance of these models favorably compare with models built with a larger set of proprietary logD data.

Using LC Retention Times in Organic Structure Determination: Drug Metabolite Identification.

BACKGROUND: There is a continued need for improvements in the efficiency of metabolite structure elucidation. OBJECTIVE: We propose to take LC Retention Time (RT) into consideration during the process of structure determination. METHODS: Herein, we develop a simple methodology that employs a Chromatographic Hydrophobicity Index (CHI) framework for standardizing LC conditions and introduce and utilize the concept of a predictable CHI change upon Phase 1 biotransformation (CHIbt). Through the analysis of literature examples, we offer a Quantitative Structure-Retention Relationship (QSRR) for several types of biotransformation (especially hydroxylation) using physicochemical properties (clogP, hydrogen bonding). RESULTS: The CHI system for retention indexing is shown to be practical and simple to implement. A database of CHIbt values has been created from re-incubation of 3 compounds and from analysis of an additional 17 datasets from the literature. Application of this database is illustrated. CONCLUSION: In our experience, this simple methodology allows complementing the discovery efforts that saves resources for in-depth characterization using NMR.

Why Decreasing Lipophilicity Alone Is Often Not a Reliable Strategy for Extending IV Half-life.

The optimization of the pharmacokinetic profile of a drug is one of the crucial aspects of medicinal chemistry campaigns. When efficacy is driven by a continuous coverage of the minimum efficacious plasma concentration, half-life must be optimized to achieve the optimal pharmacokinetic profile. The consensus in the field is that decreasing clearance, as opposed to increasing volume of distribution, is a better strategy to prolong half-life. While both the pharmacokinetic theory and the need for an optimal safety profile support this approach, this needs to be integrated with practical indications concerning the strategy to optimize clearance. This work presents an extensive analysis of Genentech's in vitro and in vivo rat pharmacokinetic data, which highlights how half-life optimization through simple modulation of lipophilicity is generally not a successful strategy. Decreasing lipophilicity without addressing a metabolic soft-spot will often lead to both lower clearance and lower volume of distribution without extending half-life.

chemalot and chemalot_knime: Command line programs as workflow tools for drug discovery.

BACKGROUND: Analyzing files containing chemical information is at the core of cheminformatics. Each analysis may require a unique workflow. This paper describes the chemalot and chemalot_knime open source packages. Chemalot is a set of command line programs with a wide range of functionalities for cheminformatics. The chemalot_knime package allows command line programs that read and write SD files from stdin and to stdout to be wrapped into KNIME nodes. The combination of chemalot and chemalot_knime not only facilitates the compilation and maintenance of sequences of command line programs but also allows KNIME workflows to take advantage of the compute power of a LINUX cluster. RESULTS: Use of the command line programs is demonstrated in three different workflow examples: (1) A workflow to create a data file with project-relevant data for structure-activity or property analysis and other type of investigations, (2) The creation of a quantitative structure-property-relationship model using the command line programs via KNIME nodes, and (3) The analysis of strain energy in small molecule ligand conformations from the Protein Data Bank database. CONCLUSIONS: The chemalot and chemalot_knime packages provide lightweight and powerful tools for many tasks in cheminformatics. They are easily integrated with other open source and commercial command line tools and can be combined to build new and even more powerful tools. The chemalot_knime package facilitates the generation and maintenance of user-defined command line workflows, taking advantage of the graphical design capabilities in KNIME. Graphical abstract Example KNIME workflow with chemalot nodes and the corresponding command line pipe.

Sustainable Practices in Medicinal Chemistry Part 2: Green by Design.

With the development of ever-expanding synthetic methodologies, a medicinal chemist's toolkit continues to swell. However, with finite time and resources as well as a growing understanding of our field's environment impact, it is critical to refine what can be made to what should be made. This review seeks to highlight multiple cheminformatic approaches in drug discovery that can influence and triage design and execution impacting the likelihood of rapidly generating high-value molecules in a more sustainable manner. This strategy gives chemists the tools to design and refine vast libraries, stress "druglikeness", and rapidly identify SAR trends. Project success, i.e., identification of a clinical candidate, is then reached faster with fewer molecules with the farther-reaching ramification of using fewer resources and generating less waste, thereby helping "green" our field.

Synthesis and evaluation of a series of 4-azaindole-containing p21-activated kinase-1 inhibitors.

A series of 4-azaindole-containing p21-activated kinase-1 (PAK1) inhibitors was prepared with the goal of improving physicochemical properties relative to an indole starting point. Indole 1 represented an attractive, non-basic scaffold with good PAK1 affinity and cellular potency but was compromised by high lipophilicity (clogD=4.4). Azaindole 5 was designed as an indole surrogate with the goal of lowering logD and resulted in equipotent PAK1 inhibition with a 2-fold improvement in cellular potency over 1. Structure-activity relationship studies around 5 identified additional 4-azaindole analogs with superior PAK1 biochemical activity (Ki <10nM) and up to 24-fold selectivity for group I over group II PAKs. Compounds from this series showed enhanced permeability, improved aqueous solubility, and lower plasma protein binding over indole 1. The improvement in physicochemical properties translated to a 20-fold decrease in unbound clearance in mouse PK studies for azaindole 5 relative to indole 1.

Chemically Diverse Group I p21-Activated Kinase (PAK) Inhibitors Impart Acute Cardiovascular Toxicity with a Narrow Therapeutic Window.

p21-activated kinase 1 (PAK1) has an important role in transducing signals in several oncogenic pathways. The concept of inhibiting this kinase has garnered significant interest over the past decade, particularly for targeting cancers associated with PAK1 amplification. Animal studies with the selective group I PAK (pan-PAK1, 2, 3) inhibitor G-5555 from the pyrido[2,3-d]pyrimidin-7-one class uncovered acute toxicity with a narrow therapeutic window. To attempt mitigating the toxicity, we introduced significant structural changes, culminating in the discovery of the potent pyridone side chain analogue G-9791. Mouse tolerability studies with this compound, other members of this series, and compounds from two structurally distinct classes revealed persistent toxicity and a correlation of minimum toxic concentrations and PAK1/2 mediated cellular potencies. Broad screening of selected PAK inhibitors revealed PAK1, 2, and 3 as the only overlapping targets. Our data suggest acute cardiovascular toxicity resulting from the inhibition of PAK2, which may be enhanced by PAK1 inhibition, and cautions against continued pursuit of pan-group I PAK inhibitors in drug discovery.

Design of Selective PAK1 Inhibitor G-5555: Improving Properties by Employing an Unorthodox Low-pK a Polar Moiety.

Signaling pathways intersecting with the p21-activated kinases (PAKs) play important roles in tumorigenesis and cancer progression. By recognizing that the limitations of FRAX1036 (1) were chiefly associated with the highly basic amine it contained, we devised a mitigation strategy to address several issues such as hERG activity. The 5-amino-1,3-dioxanyl moiety was identified as an effective means of reducing pK a and logP simultaneously. When positioned properly within the scaffold, this group conferred several benefits including potency, pharmacokinetics, and selectivity. Mouse xenograft PK/PD studies were carried out using an advanced compound, G-5555 (12), derived from this approach. These studies concluded that dose-dependent pathway modulation was achievable and paves the way for further in vivo investigations of PAK1 function in cancer and other diseases.

Structure-Guided Design of Group I Selective p21-Activated Kinase Inhibitors.

The p21-activated kinases (PAKs) play important roles in cytoskeletal organization, cellular morphogenesis, and survival and have generated significant attention as potential therapeutic targets for cancer. Following a high-throughput screen, we identified an aminopyrazole scaffold-based series that was optimized to yield group I selective PAK inhibitors. A structure-based design effort aimed at targeting the ribose pocket for both potency and selectivity led to much-improved group I vs II selectivity. Early lead compounds contained a basic primary amine, which was found to be a major metabolic soft spot with in vivo clearance proceeding predominantly via N-acetylation. We succeeded in identifying replacements with improved metabolic stability, leading to compounds with lower in vivo rodent clearance and excellent group I PAK selectivity.

Leveraging the Pre-DFG Residue Thr-406 To Obtain High Kinase Selectivity in an Aminopyrazole-Type PAK1 Inhibitor Series.

To increase kinase selectivity in an aminopyrazole-based PAK1 inhibitor series, analogues were designed to interact with the PAK1 deep-front pocket pre-DFG residue Thr-406, a residue that is hydrophobic in most kinases. This goal was achieved by installing lactam head groups to the aminopyrazole hinge binding moiety. The corresponding analogues represent the most kinase selective ATP-competitive Group I PAK inhibitors described to date. Hydrogen bonding with the Thr-406 side chain was demonstrated by X-ray crystallography, and inhibitory activities, particularly against kinases with hydrophobic pre-DFG residues, were mitigated. Leveraging hydrogen bonding side chain interactions with polar pre-DFG residues is unprecedented, and similar strategies should be applicable to other appropriate kinases.

An integrated suite of modeling tools that empower scientists in structure- and property-based drug design.

Structure- and property-based drug design is an integral part of modern drug discovery, enabling the design of compounds aimed at improving potency and selectivity. However, building molecules using desktop modeling tools can easily lead to poor designs that appear to form many favorable interactions with the protein's active site. Although a proposed molecule looks good on screen and appears to fit into the protein site X-ray crystal structure or pharmacophore model, doing so might require a high-energy small molecule conformation, which would likely be inactive. To help scientists make better design decisions, we have built integrated, easy-to-use, interactive software tools to perform docking experiments, de novo design, shape and pharmacophore based database searches, small molecule conformational analysis and molecular property calculations. Using a combination of these tools helps scientists in assessing the likelihood that a designed molecule will be active and have desirable drug metabolism and pharmacokinetic properties. Small molecule discovery success requires project teams to rapidly design and synthesize potent molecules with good ADME properties. Empowering scientists to evaluate ideas quickly and make better design decisions with easy-to-access and easy-to-understand software on their desktop is now a key part of our discovery process.

A probabilistic method to report predictions from a human liver microsomes stability QSAR model: a practical tool for drug discovery.

Using data from the in vitro liver microsomes metabolic stability assay, we have developed QSAR models to predict in vitro human clearance. Models were trained using in house high-throughput assay data reported as the predicted human hepatic clearance by liver microsomes or pCLh. Machine learning regression methods were used to generate the models. Model output for a given molecule was reported as its probability of being metabolically stable, thus allowing for synthesis prioritization based on this prediction. Use of probability, instead of the regression value or categories, has been found to be an efficient way for both reporting and assessing predictions. Model performance is evaluated using prospective validation. These models have been integrated into a number of desktop tools, and the models are routinely used to prioritize the synthesis of compounds. We discuss two therapeutic projects at Genentech that exemplify the benefits of a probabilistic approach in applying the models. A three-year retrospective analysis of measured liver microsomes stability data on all registered compounds at Genentech reveals that the use of these models has resulted in an improved metabolic stability profile of synthesized compounds.

The evaluation of 25 chiral stationary phases and the utilization of sub-2.0µm coated polysaccharide chiral stationary phases via supercritical fluid chromatography.

A rapid screening method to identify the best conditions for chiral separations is described. We analyzed a representative set of 80 racemic compounds against 25 different chiral stationary phases with three different mobile phases to identify the combination of columns and mobile phases that will separate the most compounds on the initial screen. While the OD separated the largest number of compounds, we found the best combination of six columns to be the AD, AS, AY, CC4, ID and Whelk-O1. The second team included the CCC, Cellulose-1, Cellulose-3 or OJ, IA, IE and IF. All 80 compounds were separated with a resolution range of 0.65-15.36. Screening the covalently bonded phases provided separation for 79 of the 80 compounds. We also found ethanol (0.1% NH4OH) separated more compounds than methanol (0.1% NH4OH) or isopropanol (0.1% NH4OH). As part of this study, we also compared the effectiveness of stationary phases that have the same chiral selector. Finally, we demonstrated the effectiveness of using a fast, 1.5-min screening method that utilizes a 1.7µm coated polysaccharide chiral stationary phase.

Identification of C-2 hydroxyethyl imidazopyrrolopyridines as potent JAK1 inhibitors with favorable physicochemical properties and high selectivity over JAK2.

Herein we report on the structure-based discovery of a C-2 hydroxyethyl moiety which provided consistently high levels of selectivity for JAK1 over JAK2 to the imidazopyrrolopyridine series of JAK1 inhibitors. X-ray structures of a C-2 hydroxyethyl analogue in complex with both JAK1 and JAK2 revealed differential ligand/protein interactions between the two isoforms and offered an explanation for the observed selectivity. Analysis of historical data from related molecules was used to develop a set of physicochemical compound design parameters to impart desirable properties such as acceptable membrane permeability, potent whole blood activity, and a high degree of metabolic stability. This work culminated in the identification of a highly JAK1 selective compound (31) exhibiting favorable oral bioavailability across a range of preclinical species and robust efficacy in a rat CIA model.

Physicochemical and DMPK in silico models: facilitating their use by medicinal chemists.

It is known that the developability of drugs is related to their physicochemical and DMPK properties. Given the time and expense involved in discovering and developing new drugs, maximizing the chance of success by calculating properties ahead of chemical synthesis and testing, and only acting on those candidates whose properties fall into a desired range, would seem to make sense. This paper provides an overview of calculable physicochemical and DMPK properties, an assessment of their relative difficulty of their calculation and accuracy, and available software. Methods companies have employed to communicate results will be discussed, including the use of composite scoring functions and ranking schemes. Calculations do no good if chemists will not use them to prioritize synthesis decisions. Strategies are presented for facilitating model usage. An approach adopted at Genentech for presenting results that involves the close coupling of property calculations with 3D structure based drug design is described.

Potent and selective aminopyrimidine-based B-Raf inhibitors with favorable physicochemical and pharmacokinetic properties.

Recent clinical data provided proof-of-concept for selective B-Raf inhibitors in treatment of B-Raf(V600E) mutant melanoma. Pyrazolopyridine-type B-Raf inhibitors previously described by the authors are potent and selective but exhibit low solubility requiring the use of amorphous dispersion-based formulation for achieving efficacious drug exposures. Through structure-based design, we discovered a new class of highly potent aminopyrimidine-based B-Raf inhibitors with improved solubility and pharmacokinetic profiles. The hinge binding moiety possesses a basic center imparting high solubility at gastric pH, addressing the dissolution limitation observed with our previous series. In our search for an optimal linker-hinge binding moiety system, amide-linked thieno[3,2-d]pyrimidine analogues 32 and 35 (G945), molecules with desirable physicochemical properties, emerged as lead compounds with strong efficacy in a B-Raf(V600E) mutant mouse xenograft model. Synthesis, SAR, lead selection, and evaluation of key compounds in animal studies will be described.

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.

Pyrazolopyridine Inhibitors of B-Raf(V600E). Part 1: The Development of Selective, Orally Bioavailable, and Efficacious Inhibitors.

The V600E mutation of B-Raf kinase results in constitutive activation of the MAPK signaling pathway and is present in approximately 7% of all cancers. Using structure-based design, a novel series of pyrazolopyridine inhibitors of B-Raf(V600E) was developed. Optimization led to the identification of 3-methoxy pyrazolopyridines 17 and 19, potent, selective, and orally bioavailable agents that inhibited tumor growth in a mouse xenograft model driven by B-Raf(V600E) with no effect on body weight. On the basis of their in vivo efficacy and preliminary safety profiles, 17 and 19 were selected for further preclinical evaluation.

Formation of a quinoneimine intermediate of 4-fluoro-N-methylaniline by FMO1: carbon oxidation plus defluorination.

Here, we report on the mechanism by which flavin-containing monooxygenase 1 (FMO1) mediates the formation of a reactive intermediate of 4-fluoro-N-methylaniline. FMO1 catalyzed a carbon oxidation reaction coupled with defluorination that led to the formation of 4-N-methylaminophenol, which was a reaction first reported by Boersma et al. (Boersma et al. (1993) Drug Metab. Dispos. 21 , 218 - 230). We propose that a labile 1-fluoro-4-(methylimino)cyclohexa-2,5-dienol intermediate was formed leading to an electrophilic quinoneimine intermediate. The identification of N-acetylcysteine adducts by LC-MS/MS and NMR further supports the formation of a quinoneimine intermediate. Incubations containing stable labeled oxygen (H(2)(18)O or (18)O(2)) and ab initio calculations were performed to support the proposed reaction mechanism.

RAF inhibitors prime wild-type RAF to activate the MAPK pathway and enhance growth.

Activating mutations in KRAS and BRAF are found in more than 30% of all human tumours and 40% of melanoma, respectively, thus targeting this pathway could have broad therapeutic effects. Small molecule ATP-competitive RAF kinase inhibitors have potent antitumour effects on mutant BRAF(V600E) tumours but, in contrast to mitogen-activated protein kinase kinase (MEK) inhibitors, are not potent against RAS mutant tumour models, despite RAF functioning as a key effector downstream of RAS and upstream of MEK. Here we show that ATP-competitive RAF inhibitors have two opposing mechanisms of action depending on the cellular context. In BRAF(V600E) tumours, RAF inhibitors effectively block the mitogen-activated protein kinase (MAPK) signalling pathway and decrease tumour growth. Notably, in KRAS mutant and RAS/RAF wild-type tumours, RAF inhibitors activate the RAF-MEK-ERK pathway in a RAS-dependent manner, thus enhancing tumour growth in some xenograft models. Inhibitor binding activates wild-type RAF isoforms by inducing dimerization, membrane localization and interaction with RAS-GTP. These events occur independently of kinase inhibition and are, instead, linked to direct conformational effects of inhibitors on the RAF kinase domain. On the basis of these findings, we demonstrate that ATP-competitive kinase inhibitors can have opposing functions as inhibitors or activators of signalling pathways, depending on the cellular context. Furthermore, this work provides new insights into the therapeutic use of ATP-competitive RAF inhibitors.