High-throughputinvitroprofiling assays: lessons learnt from experiences at Novartis

Metabophore-mediated retro-metabolic ('MeMeReMe') approach in drug design

Preclinical toxicity assessments of new drugs require the use of in silico prediction techniques as ethics, cost, time, and complexity limit in vitro and in vivo methods. This review discusses the fundamental concepts of biophores especially toxicophores and their detection methodologies, tools and techniques, as well as ongoing challenges, and methods for overcoming them. This will guide the design community in manipulating lead compounds via a pre-determined pathway based on the MeMeReMe approach. The ideas discussed will be useful both for predicting toxicity and for de-risking leads through optimization.

NITD-688, a pan-serotype inhibitor of the dengue virus NS4B protein, shows favorable pharmacokinetics and efficacy in preclinical animal models

Dengue virus (DENV) is a mosquito-borne flavivirus that poses a threat to public health, yet no antiviral drug is available. We performed a high-throughput phenotypic screen using the Novartis compound library and identified candidate chemical inhibitors of DENV. This chemical series was optimized to improve properties such as anti-DENV potency and solubility. The lead compound, NITD-688, showed strong potency against all four serotypes of DENV and demonstrated excellent oral efficacy in infected AG129 mice. There was a 1.44-log reduction in viremia when mice were treated orally at 30 milligrams per kilogram twice daily for 3 days starting at the time of infection. NITD-688 treatment also resulted in a 1.16-log reduction in viremia when mice were treated 48 hours after infection. Selection of resistance mutations and binding studies with recombinant proteins indicated that the nonstructural protein 4B is the target of NITD-688. Pharmacokinetic studies in rats and dogs showed a long elimination half-life and good oral bioavailability. Extensive in vitro safety profiling along with exploratory rat and dog toxicology studies showed that NITD-688 was well tolerated after 7-day repeat dosing, demonstrating that NITD-688 may be a promising preclinical candidate for the treatment of dengue.

Machine Learning Strategies When Transitioning between Biological Assays

Machine learning is widely used in drug development to predict activity in biological assays based on chemical structure. However, the process of transitioning from one experimental setup to another for the same biological endpoint has not been extensively studied. In a retrospective study, we here explore different modeling strategies of how to combine data from the old and new assays when training conformal prediction models using data from hERG and Na_V assays. We suggest to continuously monitor the validity and efficiency of models as more data is accumulated from the new assay and select a modeling strategy based on these metrics. In order to maximize the utility of data from the old assay, we propose a strategy that augments the proper training set of an inductive conformal predictor by adding data from the old assay but only having data from the new assay in the calibration set, which results in valid (well-calibrated) models with improved efficiency compared to other strategies. We study the results for varying sizes of new and old assays, allowing for discussion of different practical scenarios. We also conclude that our proposed assay transition strategy is more beneficial, and the value of data from the new assay is higher, for the harder case of regression compared to classification problems.

A Cryptosporidium PI(4)K inhibitor is a drug candidate for cryptosporidiosis

Diarrhoeal disease is responsible for 8.6% of global child mortality. Recent epidemiological studies found the protozoan parasite Cryptosporidium to be a leading cause of paediatric diarrhoea, with particularly grave impact on infants and immunocompromised individuals. There is neither a vaccine nor an effective treatment. Here we establish a drug discovery process built on scalable phenotypic assays and mouse models that take advantage of transgenic parasites. Screening a library of compounds with anti-parasitic activity, we identify pyrazolopyridines as inhibitors of Cryptosporidium parvum and Cryptosporidium hominis. Oral treatment with the pyrazolopyridine KDU731 results in a potent reduction in intestinal infection of immunocompromised mice. Treatment also leads to rapid resolution of diarrhoea and dehydration in neonatal calves, a clinical model of cryptosporidiosis that closely resembles human infection. Our results suggest that the Cryptosporidium lipid kinase PI(4)K (phosphatidylinositol-4-OH kinase) is a target for pyrazolopyridines and that KDU731 warrants further preclinical evaluation as a drug candidate for the treatment of cryptosporidiosis.

The establishment of a drug-discovery screening pipeline for cryptosporidiosis, and identification of pyrazolopyridines as selective ATP-competitive inhibitors of the Cryptosporidium lipid kinase PI(4)K.

The apicomplexan parasite Cryptosporidium is a leading cause of paediatric diarrhoea, with high mortality in infants and individuals with weak immune systems. Here, the authors report the establishment of a drug discovery screening pipeline for cryptosporidiosis, and identify pyrazolopyridines as selective ATP-competitive inhibitors of the Cryptosporidium lipid kinase PI(4)K. The lead candidate, KDU731, inhibits growth of C. parvum and C. hominis in vitro, and demonstrated in vivo efficacy in immunocompromised mice and neonatal calves (a clinical model of human disease). Moreover, KDU731 meets a broad range of safety and pharmacology criteria, and has potential as a much-needed therapeutic for treatment of cryptosporidiosis.

Predictions of the physicochemical properties of amino acid side chain analogs using molecular simulation

A candidate drug compound is released for clinical trails (in vivo activity) only if its physicochemical properties meet desirable bioavailability and partitioning criteria.

Direct inhibitors of InhA are active against Mycobacterium tuberculosis

New chemotherapeutic agents are urgently required to combat the global spread of multidrug-resistant tuberculosis (MDR-TB). The mycobacterial enoyl reductase InhA is one of the few clinically validated targets in tuberculosis drug discovery. We report the identification of a new class of direct InhA inhibitors, the 4-hydroxy-2-pyridones, using phenotypic high-throughput whole-cell screening. This class of orally active compounds showed potent bactericidal activity against common isoniazid-resistant TB clinical isolates. Biophysical studies revealed that 4-hydroxy-2-pyridones bound specifically to InhA in an NADH (reduced form of nicotinamide adenine dinucleotide)-dependent manner and blocked the enoyl substrate-binding pocket. The lead compound NITD-916 directly blocked InhA in a dose-dependent manner and showed in vivo efficacy in acute and established mouse models of Mycobacterium tuberculosis infection. Collectively, our structural and biochemical data open up new avenues for rational structure-guided optimization of the 4-hydroxy-2-pyridone class of compounds for the treatment of MDR-TB.

Relating Essential Proteins to Drug Side-Effects Using Canonical Component Analysis: A Structure-Based Approach

The molecular mechanism of many drug side-effects is unknown and difficult to predict. Previous methods for explaining side-effects have focused on known drug targets and their pathways. However, low affinity binding to proteins that are not usually considered drug targets may also drive side-effects. In order to assess these alternative targets, we used the 3D structures of 563 essential human proteins systematically to predict binding to 216 drugs. We first benchmarked our affinity predictions with available experimental data. We then combined singular value decomposition and canonical component analysis (SVD-CCA) to predict side-effects based on these novel target profiles. Our method predicts side-effects with good accuracy (average AUC: 0.82 for side effects present in <50% of drug labels). We also noted that side-effect frequency is the most important feature for prediction and can confound efforts at elucidating mechanism; our method allows us to remove the contribution of frequency and isolate novel biological signals. In particular, our analysis produces 2768 triplet associations between 50 essential proteins, 99 drugs, and 77 side-effects. Although experimental validation is difficult because many of our essential proteins do not have validated assays, we nevertheless attempted to validate a subset of these associations using experimental assay data. Our focus on essential proteins allows us to find potential associations that would likely be missed if we used recognized drug targets. Our associations provide novel insights about the molecular mechanisms of drug side-effects and highlight the need for expanded experimental efforts to investigate drug binding to proteins more broadly.

Calculation and application of activity discriminants in lead optimization

We present a technique for computing activity discriminants of in vitro (pharmacological, DMPK, and safety) assays and the application to the prediction of in vitro activities of proposed synthetic targets during the lead optimization phase of drug discovery projects. This technique emulates how medicinal chemists perform SAR analysis and activity prediction. The activity discriminants that are functions of 6 commonly used medicinal chemistry descriptors can be interpreted easily by medicinal chemists. Further, visualization with Spotfire allows medicinal chemists to analyze how the query molecule is related to compounds tested previously, and to evaluate easily the relevance of the activity discriminants to the activities of the query molecule. Validation with all compounds synthesized and tested in AstraZeneca Wilmington since 2006 demonstrates that this approach is useful for prioritizing new synthetic targets for synthesis.

Recent advances in physicochemical and ADMET profiling in drug discovery

The drastic increase in the cost for discovering and developing a new drug along with the high attrition rate of development candidates led to shifting drug-discovery strategy to parallel assessment of comprehensive drug physicochemical, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties alongside efficacy. With the proposal of a profiling paradigm and utilization of integrated risk assessment, one can exponentially enhance the predictive power of in vitro tools by taking into consideration the interplay among profiling parameters. In particular, this article will review recent advances in accurate assessment of solubility and other physicochemical parameters. The proper interpretation of these experimental data is crucial for rapid and meaningful risk assessment and rational optimization of drug candidates in drug discovery. The impact of these tools on assisting drug-discovery teams in establishing in vitro-in vivo correlation (IVIVC) as well as structure-property relationship (SPR) will be presented.