Selective and brain-penetrant HCN1 inhibitors reveal links between synaptic integration, cortical function, and working memory

Hyperpolarization-activated and cyclic-nucleotide-gated 1 (HCN1) ion channels are proposed to be critical for cognitive function through regulation of synaptic integration. However, resolving the precise role of HCN1 in neurophysiology and exploiting its therapeutic potential has been hampered by minimally selective antagonists with poor potency and limited in vivo efficiency. Using automated electrophysiology in a small-molecule library screen and chemical optimization, we identified a primary carboxamide series of potent and selective HCN1 inhibitors with a distinct mode of action. In cognition-relevant brain circuits, selective inhibition of native HCN1 produced on-target effects, including enhanced excitatory postsynaptic potential summation, while administration of a selective HCN1 inhibitor to rats recovered decrement working memory. Unlike prior non-selective HCN antagonists, selective HCN1 inhibition did not alter cardiac physiology in human atrial cardiomyocytes or in rats. Collectively, selective HCN1 inhibitors described herein unmask HCN1 as a potential target for the treatment of cognitive dysfunction in brain disorders.

A Machine Learning Framework to Improve Rat Clearance Predictions and Inform Physiologically Based Pharmacokinetic Modeling

During drug discovery and development, achieving appropriate pharmacokinetics is key to establishment of the efficacy and safety of new drugs. Physiologically based pharmacokinetic (PBPK) models integrating in vitro-to-in vivo extrapolation have become an essential in silico tool to achieve this goal. In this context, the most important and probably most challenging pharmacokinetic parameter to estimate is the clearance. Recent work on high-throughput PBPK modeling during drug discovery has shown that a good estimate of the unbound intrinsic clearance (CLint,u,) is the key factor for useful PBPK application. In this work, three different machine learning-based strategies were explored to predict the rat CLint,u as the input into PBPK. Therefore, in vivo and in vitro data was collected for a total of 2639 proprietary compounds. The strategies were compared to the standard in vitro bottom-up approach. Using the well-stirred liver model to back-calculate in vivo CLint,u from in vivo rat clearance and then training a machine learning model on this CLint,u led to more accurate clearance predictions (absolute average fold error (AAFE) 3.1 in temporal cross-validation) than the bottom-up approach (AAFE 3.6-16, depending on the scaling method) and has the advantage that no experimental in vitro data is needed. However, building a machine learning model on the bias between the back-calculated in vivo CLint,u and the bottom-up scaled in vitro CLint,u also performed well. For example, using unbound hepatocyte scaling, adding the bias prediction improved the AAFE in the temporal cross-validation from 16 for bottom-up to 2.9 together with the bias prediction. Similarly, the log Pearson r2 improved from 0.1 to 0.29. Although it would still require in vitro measurement of CLint,u., using unbound scaling for the bottom-up approach, the need for correction of the fu,inc by fu,p data is circumvented. While the above-described ML models were built on all data points available per approach, it is discussed that evaluation comparison across all approaches could only be performed on a subset because ca. 75% of the molecules had missing or unquantifiable measurements of the fraction unbound in plasma or in vitro unbound intrinsic clearance, or they dropped out due to the blood-flow limitation assumed by the well-stirred model. Advantageously, by predicting CLint,u as the input into PBPK, existing workflows can be reused and the prediction of the in vivo clearance and other PK parameters can be improved.

Phenyl bioisosteres in medicinal chemistry: discovery of novel γ-secretase modulators as a potential treatment for Alzheimer's disease

Phenyl rings are one of the most prevalent structural moieties in active pharmaceutical ingredients, even if they often contribute to poor physico-chemical properties. Herein, we propose the use of a bridged piperidine (BP) moiety as a phenyl bioisostere, which could also be seen as a superior phenyl alternative as it led to strongly improved drug like properties, in terms of solubility and lipophilicity. Additionally, this BP moiety compares favorably to the recently reported saturated phenyl bioisosteres. We applied this concept to our γ-secretase modulator (GSM) project for the potential treatment of Alzheimer's disease delivering clinical candidates.

Discovery of RO7185876, a Highly Potent γ-Secretase Modulator (GSM) as a Potential Treatment for Alzheimer's Disease

γ-Secretase (GS) is a key target for the potential treatment of Alzheimer's disease. While inhibiting GS led to serious side effects, its modulation holds a lot of potential to deliver a safe treatment. Herein, we report the discovery of a potent and selective gamma secretase modulator (GSM) (S)-3 (RO7185876), belonging to a novel chemical class, the triazolo-azepines. This compound demonstrates an excellent in vitro and in vivo DMPK profile. Furthermore, based on its in vivo efficacy in a pharmacodynamic mouse model and the outcome of the dose range finding (DRF) toxicological studies in two species, this compound was selected to undergo entry in human enabling studies (e.g., GLP toxicology and scale up activities).

Understanding Molecular Drivers of Melanin Binding To Support Rational Design of Small Molecule Ophthalmic Drugs

Binding of drugs to ocular melanin is a prominent biological phenomenon that affects the local pharmacokinetics and pharmacodynamics in the eye. In this work, we report on the development of in vitro and in silico tools for an early assessment and prediction of melanin binding properties of small molecules. A robust high-throughput assay has been established to study the binding of large sets of compounds to melanin. The extremely randomized trees approach was used to develop an in silico model able to predict the extent of melanin binding from the molecular properties of the compounds. After the last iteration of the model, strong melanin binders could prospectively be identified with 91% accuracy. On the basis of in vitro data generated for approximately 3400 chemically diverse drug-like small molecules, pronounced correlations were observed between the extent of melanin binding and the basicity, lipophilicity, and aromaticity of the compounds.

Discovery of Risdiplam, a Selective Survival of Motor Neuron-2 ( SMN2) Gene Splicing Modifier for the Treatment of Spinal Muscular Atrophy (SMA)

SMA is an inherited disease that leads to loss of motor function and ambulation and a reduced life expectancy. We have been working to develop orally administrated, systemically distributed small molecules to increase levels of functional SMN protein. Compound 2 was the first SMN2 splicing modifier tested in clinical trials in healthy volunteers and SMA patients. It was safe and well tolerated and increased SMN protein levels up to 2-fold in patients. Nevertheless, its development was stopped as a precautionary measure because retinal toxicity was observed in cynomolgus monkeys after chronic daily oral dosing (39 weeks) at exposures in excess of those investigated in patients. Herein, we describe the discovery of 1 (risdiplam, RG7916, RO7034067) that focused on thorough pharmacology, DMPK and safety characterization and optimization. This compound is undergoing pivotal clinical trials and is a promising medicine for the treatment of patients in all ages and stages with SMA.

Designing Multi-target Compound Libraries with Gaussian Process Models

We present the application of machine learning models to selecting G protein-coupled receptor (GPCR)-focused compound libraries. The library design process was realized by ant colony optimization. A proprietary Boehringer-Ingelheim reference set consisting of 3519 compounds tested in dose-response assays at 11 GPCR targets served as training data for machine learning and activity prediction. We compared the usability of the proprietary data with a public data set from ChEMBL. Gaussian process models were trained to prioritize compounds from a virtual combinatorial library. We obtained meaningful models for three of the targets (5-HT2c , MCH, A1), which were experimentally confirmed for 12 of 15 selected and synthesized or purchased compounds. Overall, the models trained on the public data predicted the observed assay results more accurately. The results of this study motivate the use of Gaussian process regression on public data for virtual screening and target-focused compound library design.

Multidimensional de novo design reveals 5-HT2B receptor-selective ligands

We report a multi-objective de novo design study driven by synthetic tractability and aimed at the prioritization of computer-generated 5-HT2B receptor ligands with accurately predicted target-binding affinities. Relying on quantitative bioactivity models we designed and synthesized structurally novel, selective, nanomolar, and ligand-efficient 5-HT2B modulators with sustained cell-based effects. Our results suggest that seamless amalgamation of computational activity prediction and molecular design with microfluidics-assisted synthesis enables the swift generation of small molecules with the desired polypharmacology.

Multi-objective molecular de novo design by adaptive fragment prioritization

We present the development and application of a computational molecular de novo design method for obtaining bioactive compounds with desired on- and off-target binding. The approach translates the nature-inspired concept of ant colony optimization to combinatorial building block selection. By relying on publicly available structure-activity data, we developed a predictive quantitative polypharmacology model for 640 human drug targets. By taking reductive amination as an example of a privileged reaction, we obtained novel subtype-selective and multitarget-modulating dopamine D4 antagonists, as well as ligands selective for the sigma-1 receptor with accurately predicted affinities. The nanomolar potencies of the hits obtained, their high ligand efficiencies, and an overall success rate of 90 % demonstrate that this ligand-based computer-aided molecular design method may guide target-focused combinatorial chemistry.

Combining on-chip synthesis of a focused combinatorial library with computational target prediction reveals imidazopyridine GPCR ligands

Using the example of the Ugi three-component reaction we report a fast and efficient microfluidic-assisted entry into the imidazopyridine scaffold, where building block prioritization was coupled to a new computational method for predicting ligand-target associations. We identified an innovative GPCR-modulating combinatorial chemotype featuring ligand-efficient adenosine A1/2B and adrenergic α1A/B receptor antagonists. Our results suggest the tight integration of microfluidics-assisted synthesis with computer-based target prediction as a viable approach to rapidly generate bioactivity-focused combinatorial compound libraries with high success rates.

Exhaustive proteome mining for functional MHC-I ligands

We present the development and application of a new machine-learning approach to exhaustively and reliably identify major histocompatibility complex class I (MHC-I) ligands among all 20(8) octapeptides and in genome-derived proteomes of Mus musculus , influenza A H3N8, and vesicular stomatitis virus (VSV). Focusing on murine H-2K(b), we identified potent octapeptides exhibiting direct MHC-I binding and stabilization on the surface of TAP-deficient RMA-S cells. Computationally identified VSV-derived peptides induced CD8(+) T-cell proliferation after VSV-infection of mice. The study demonstrates that high-level machine-learning models provide a unique access to rationally designed peptides and a promising approach toward "reverse vaccinology".

Drugs by numbers: reaction-driven de novo design of potent and selective anticancer leads

A potent and selective inhibitor of the anticancer target Polo-like kinase 1 was found by computer-based molecular design. This type II kinase inhibitor was synthesized as suggested by the design software DOGS and exhibited significant antiproliferative effects against HeLa cells without affecting nontransformed cells. The study provides a proof-of-concept for reaction-based de novo design as a leading tool for drug discovery.

Adaptive peptide design

Computer algorithms help in the identification and optimization of peptides with desired structure and function. We provide an overview of the current focus of our research group in this field, highlighting innovative methods for peptide representation and de novo peptide generation. Our evolutionary molecular design cycle contains structure-activity relationship modeling by machine-learning methods, virtual peptide generation, activity prediction, peptide syntheses, as well as biophysical and biochemical activity determination. Such interplay between computer-assisted peptide generation and scoring with real laboratory experiments enables rapid feedback throughout the design cycle so that adaptive optimization can take place. Selected practical applications are reviewed including the design of new immunomodulatory MHC-I binding peptides and antimicrobial peptides.

Nonlinear dimensionality reduction and mapping of compound libraries for drug discovery

Visualization of 'chemical space' and compound distributions has received much attraction by medicinal chemists as it may help to intuitively comprehend pharmaceutically relevant molecular features. It has been realized that for meaningful feature extraction from complex multivariate chemical data, such as compound libraries represented by many molecular descriptors, nonlinear projection techniques are required. Recent advances in machine-learning and artificial intelligence have resulted in a transfer of such methods to chemistry. We provide an overview of prominent visualization methods based on nonlinear dimensionality reduction, and highlight applications in drug discovery. Emphasis is on neural network techniques, kernel methods and stochastic embedding approaches, which have been successfully used for ligand-based virtual screening, SAR landscape analysis, combinatorial library design, and screening compound selection.

Rationalizing tight ligand binding through cooperative interaction networks

Small modifications of the molecular structure of a ligand sometimes cause strong gains in binding affinity to a protein target, rendering a weakly active chemical series suddenly attractive for further optimization. Our goal in this study is to better rationalize and predict the occurrence of such interaction hot-spots in receptor binding sites. To this end, we introduce two new concepts into the computational description of molecular recognition. First, we take a broader view of noncovalent interactions and describe protein-ligand binding with a comprehensive set of favorable and unfavorable contact types, including for example halogen bonding and orthogonal multipolar interactions. Second, we go beyond the commonly used pairwise additive treatment of atomic interactions and use a small world network approach to describe how interactions are modulated by their environment. This approach allows us to capture local cooperativity effects and considerably improves the performance of a newly derived empirical scoring function, ScorpionScore. More importantly, however, we demonstrate how an intuitive visualization of key intermolecular interactions, interaction networks, and binding hot-spots supports the identification and rationalization of tight ligand binding.

Target Profile Prediction and Practical Evaluation of a Biginelli-Type Dihydropyrimidine Compound Library

We present a self-organizing map (SOM) approach to predicting macromolecular targets for combinatorial compound libraries. The aim was to study the usefulness of the SOM in combination with a topological pharmacophore representation (CATS) for selecting biologically active compounds from a virtual combinatorial compound collection, taking the multi-component Biginelli dihydropyrimidine reaction as an example. We synthesized a candidate compound from this library, for which the SOM model suggested inhibitory activity against cyclin-dependent kinase 2 (CDK2) and other kinases. The prediction was confirmed in an in vitro panel assay comprising 48 human kinases. We conclude that the computational technique may be used for ligand-based in silico pharmacology studies, off-target prediction, and drug re-purposing, thereby complementing receptor-based approaches.

Reaction-driven de novo design, synthesis and testing of potential type II kinase inhibitors

BACKGROUND

De novo design of drug-like compounds with a desired pharmacological activity profile has become feasible through innovative computer algorithms. Fragment-based design and simulated chemical reactions allow for the rapid generation of candidate compounds as blueprints for organic synthesis.

METHODS

We used a combination of complementary virtual-screening tools for the analysis of de novo designed compounds that were generated with the aim to inhibit inactive polo-like kinase 1 (Plk1), a target for the development of cancer therapeutics. A homology model of the inactive state of Plk1 was constructed and the nucleotide binding pocket conformations in the DFG-in and DFG-out state were compared. The de novo-designed compounds were analyzed using pharmacophore matching, structure-activity landscape analysis, and automated ligand docking. One compound was synthesized and tested in vitro.

RESULTS

The majority of the designed compounds possess a generic architecture present in known kinase inhibitors. Predictions favor kinases as targets of these compounds but also suggest potential off-target effects. Several bioisosteric replacements were suggested, and de novo designed compounds were assessed by automated docking for potential binding preference toward the inactive (type II inhibitors) over the active conformation (type I inhibitors) of the kinase ATP binding site. One selected compound was successfully synthesized as suggested by the software. The de novo-designed compound exhibited inhibitory activity against inactive Plk1 in vitro, but did not show significant inhibition of active Plk1 and 38 other kinases tested.

CONCLUSIONS

Computer-based de novo design of screening candidates in combination with ligand- and receptor-based virtual screening generates motivated suggestions for focused library design in hit and lead discovery. Attractive, synthetically accessible compounds can be obtained together with predicted on- and off-target profiles and desired activities.

[The transformation of gamma-tocopherol to alpha-tocopherol in the animal organism; a generational study in rats]

The biosynthesis of alpha-tocopherol, the most effective vitamer among the vitamin E-group, is found only in higher plants and microorganisms. Due to the lack of the shikimate pathway, animals are not able to synthesize alpha-tocopherol. Also not found is a whole enteral synthesis; only the conversion of dimethyletocol to trimethyletocol seems to be possible. Using four generations of rats, we sought to determine: Is a transformation of gamma-tocopherol to alpha-tocopherol in the animal body possible? Are there any differences in the transformation rates in organs, tissues, or in the entire body along the generations? Does gut flora play any role in the conversion of gamma-tocopherol? Is it possible to increase the efficiency of the transformation by supplying additional CH3-groups? Wistar rats were fed a semisynthetic basal diet, supplemented with 78.8 mg DL-gamma-tocopherol/kg in the first three generations (F1-F3). In the fourth generation (F4), some of the animals were fed a vitamin E-free diet and gamma-tocopherol (approx. 1.5 mg on alternate days) was injected s.c. Two other groups of animals received the basal diet containing additional methionine (0.25%) or choline (0.45%), as well as gamma-tocopherol (as in F1-F3). alpha- and gamma-tocopherol were analyzed by HPTLC in the whole body and in serum, liver, heart, lung, gut, gonads, and feces. The ratio of alpha-/gamma-tocopherol (micrograms/micrograms) as transformation rate and vitamin E-biopotency (microgram alpha-tocopherol equivalents/g) were calculated. Growth and fertility were normal until the fourth generation; no abnormal developments could be recognized. alpha-tocopherol was found in the whole-body as well as in all tissues and organs. In the whole-body, vitamin E-biopotency decreased 25-70% in F2 and F3. On the other hand, the increase of the transformation rate of gamma- to alpha-tocopherol amounted to 23% (F2) and 168% (F3). Highest conversion rates were found in F2 and F3 for feces, followed by gonads and lungs; the lowest rates were found for serum and liver. Due to the s.c. injection of gamma-tocopherol, feces showed a four-times lower transformation rate in F4 than in F3. There was an increase in heart, gut, lung and serum for both transformation rate and vitamin E-biopotency. These parameters could be improved also by the additional supplements of methionine and choline. Both methyl-group-donators revealed nearly the same positive effect. The results show that the animal organism can adapt to gamma-tocopherol supply over generations.(ABSTRACT TRUNCATED AT 250 WORDS)