Avoid missing pKas: High-throughput workflow using solution pH-metric in tandem with UV-metric measurements.

We describe a new high-throughput automated pKa workflow using potentiometry starting with 10 mM DMSO stock (solution pH-metric). Two approaches using either neat DMSO stock solution or removal of DMSO were evaluated with different sample amounts and cosolvent schemes. These were validated against traditional potentiometric measurements for optimal conditions. Further, we detail how high throughput solution pH-metric experiments are performed in tandem with established UV-metric measurements to capitalize on the advantages of both approaches. This new workflow maintains the sample and time savings required for measuring a large number of samples in a drug discovery setting, while avoiding "missing pKas" due to lack of sufficient UV chromophores. The combination of the two assays is key to tackle the challenges of low solubility, overlapping pKas, and preliminary assignment of pKas for Structure-Activity Relationship (SAR) understanding.

Paradoxical Increase of Permeability and Lipophilicity with the Increasing Topological Polar Surface Area within a Series of PRMT5 Inhibitors.

An imidazolone → triazolone replacement addressed the limited passive permeability of a series of protein arginine methyl transferase 5 (PRMT5) inhibitors. This increase in passive permeability was unexpected given the increase in the hydrogen bond acceptor (HBA) count and topological polar surface area (TPSA), two descriptors that are typically inversely correlated with permeability. Quantum mechanics (QM) calculations revealed that this unusual effect was due to an electronically driven disconnect between TPSA and 3D-PSA, which manifests in a reduction in overall HBA strength as indicated by the HBA moment descriptor from COSMO-RS (conductor-like screening model for real solvation). HBA moment was subsequently deployed as a design parameter leading to the discovery of inhibitors with not only improved passive permeability but also reduced P-glycoprotein (P-gp) transport. Our case study suggests that hidden polarity as quantified by TPSA-3DPSA can be rationally designed through QM calculations.

Discovery solubility measurement and assessment of small molecules with drug development in mind.

Solubility is a key physicochemical property for the success of any drug candidate. Although the methods used and their rationales for determining solubility are subject to project needs and stages along the drug discovery-drug development pipeline, an artificial boundary can exist at the discovery-development interface. This boundary results in less effective solubility knowledge sharing and data integration among scientists in both drug discovery and drug development. Herein, we present a refreshed perspective on solubility. Solubility experimentation is not a one-size-fits-all measurement; instead, we stress the importance of constructing a seamless solubility understanding of a molecule as it progresses from a new chemical entity into a drug product.

Prediction of pKa Using Machine Learning Methods with Rooted Topological Torsion Fingerprints: Application to Aliphatic Amines.

The acid-base dissociation constant, pKa, is a key parameter to define the ionization state of a compound and directly affects its biopharmaceutical profile. In this study, we developed a novel approach for pKa prediction using rooted topological torsion fingerprints in combination with five machine learning (ML) methods: random forest, partial least squares, extreme gradient boosting, lasso regression, and support vector regression. With a large and diverse set of 14 499 experimental pKa values, pKa models were developed for aliphatic amines. The models demonstrated consistently good prediction statistics and were able to generate accurate prospective predictions as validated with an external test set of 726 pKa values (RMSE 0.45, MAE 0.33, and R2 0.84 by the top model). The factors that may affect prediction accuracy and model applicability were carefully assessed. The results demonstrated that rooted topological torsion fingerprints coupled with ML methods provide a promising approach for developing accurate pKa prediction models.

Automated Supersaturation Stability Assay to Differentiate Poorly Soluble Compounds in Drug Discovery.

Increasingly, in vitro assays evaluate a compound's tendency to maintain supersaturation toward improving oral absorption. Throughput remains a challenge and only small sets of compounds are evaluated in reported studies. The present work describes an automated workflow and data analysis approach to determine supersaturation stability after 16 min. Eight increasing concentrations were targeted and supernatant concentration was measured following solvent shift in fasted-state simulated intestinal fluid. The effect of dimethyl sulfoxide both on equilibrium solubility and on induced supersaturation was addressed, whereas the change in concentration was evaluated over time. Our sample set included 24 commercial compounds, along with comparison to literature results. To demonstrate in vivo relevance of in vitro supersaturation, classification of supersaturation stability was proposed based on the target concentration achieved and the percentage of area under the curve dose proportionality in 42 preclinical and clinical studies. Eighty-one percent of low supersaturation stability compounds (target concentrations ≤50 µM) had proportionality <0.8, whereas 100% of high supersaturation stability compounds (target concentrations ≥200 µM) demonstrated proportionality ≥0.8. The robust, automated assay and its impact on dose proportionality downstream make this approach applicable in drug discovery where low-soluble compounds with otherwise attractive properties may be differentiated on the basis of supersaturation stability.

Developing Collaborative QSAR Models Without Sharing Structures.

It is widely understood that QSAR models greatly improve if more data are used. However, irrespective of model quality, once chemical structures diverge too far from the initial data set, the predictive performance of a model degrades quickly. To increase the applicability domain we need to increase the diversity of the training set. This can be achieved by combining data from diverse sources. Public data can be easily included; however, proprietary data may be more difficult to add due to intellectual property concerns. In this contribution, we will present a method for the collaborative development of linear regression models that addresses this problem. The method differs from other past approaches, because data are only shared in an aggregated form. This prohibits access to individual data points and therefore avoids the disclosure of confidential structural information. The final models are equivalent to models that were built with combined data sets.

Benefit of Retraining pKa Models Studied Using Internally Measured Data.

The ionization state of drugs influences many pharmaceutical properties such as their solubility, permeability, and biological activity. It is therefore important to understand the structure property relationship for the acid-base dissociation constant pKa during the lead optimization process to make better-informed design decisions. Computational approaches, such as implemented in MoKa, can help with this; however, they often predict with too large error especially for proprietary compounds. In this contribution, we look at how retraining helps to greatly improve prediction error. Using a longitudinal study with data measured over 15 years in a drug discovery environment, we assess the impact of model training on prediction accuracy and look at model degradation over time. Using the MoKa software, we will demonstrate that regular retraining is required to address changes in chemical space leading to model degradation over six to nine months.

Permeability diagnosis model in drug discovery: a diagnostic tool to identify the most influencing properties for gastrointestinal permeability.

Permeability is important in governing the ability of drug substances to transport across gastrointestinal membrane and also crucial for proper drug distribution to pharmacological target organs and cells, and is therefore frequently utilized in drug discovery and development. In this report, we have performed a systematic analysis, using principal component analysis on the historically measured permeability data from in-house Caco-2 and parallel artificial membrane permeability assays on discovery new chemical entities from multiple projects. This work allows for establishment of a permeability diagnosis model by purposefully identifying most influencing physicochemical properties of the permeability issues, including polarity-lipophilicity line contributed primarily by polar surface area and LogP, number of rotation bond, fractional ionization at neutral pH and efflux ratio. A number of cases were also shown to demonstrate the applicability of the current model. The analysis of the model over internal drug discovery compounds exhibited promising diagnostic and predictive power of the model. The advantages and limitation of the model as well as the integral strategy to apply it in drug discovery to guide projects for permeability-related optimization were also presented.

Imidazolopiperazines: lead optimization of the second-generation antimalarial agents.

On the basis of the initial success of optimization of a novel series of imidazolopiperazines, a second generation of compounds involving changes in the core piperazine ring was synthesized to improve antimalarial properties. These changes were carried out to further improve the potency and metabolic stability of the compounds by leveraging the outcome of a set of in vitro metabolic identification studies. The optimized 8,8-dimethyl imidazolopiperazine analogues exhibited improved potency, in vitro metabolic stability profile and, as a result, enhanced oral exposure in vivo in mice. The optimized compounds were found to be more efficacious than the current antimalarials in a malaria mouse model. They exhibit moderate oral exposure in rat pharmacokinetic studies to achieve sufficient multiples of the oral exposure at the efficacious dose in toxicology studies.

Optimization of the in vitro cardiac safety of hydroxamate-based histone deacetylase inhibitors.

Histone deacetylase (HDAC) inhibitors have shown promise in treating various forms of cancer. However, many HDAC inhibitors from diverse structural classes have been associated with QT prolongation in humans. Inhibition of the human ether a-go-go related gene (hERG) channel has been associated with QT prolongation and fatal arrhythmias. To determine if the observed cardiac effects of HDAC inhibitors in humans is due to hERG blockade, a highly potent HDAC inhibitor devoid of hERG activity was required. Starting with dacinostat (LAQ824), a highly potent HDAC inhibitor, we explored the SAR to determine the pharmacophores required for HDAC and hERG inhibition. We disclose here the results of these efforts where a high degree of pharmacophore homology between these two targets was discovered. This similarity prevented traditional strategies for mitigating hERG binding/modulation from being successful and novel approaches for reducing hERG inhibition were required. Using a hERG homology model, two compounds, 11r and 25i, were discovered to be highly efficacious with weak affinity for the hERG and other ion channels.

Imidazolopiperazines: hit to lead optimization of new antimalarial agents.

Starting from a hit series from a GNF compound library collection and based on a cell-based proliferation assay of Plasmodium falciparum, a novel imidazolopiperazine scaffold was optimized. SAR for this series of compounds is discussed, focusing on optimization of cellular potency against wild-type and drug resistant parasites and improvement of physiochemical and pharmacokinetic properties. The lead compounds in this series showed good potencies in vitro and decent oral exposure levels in vivo. In a Plasmodium berghei mouse infection model, one lead compound lowered the parasitemia level by 99.4% after administration of 100 mg/kg single oral dose and prolonged mice survival by an average of 17.0 days. The lead compounds were also well-tolerated in the preliminary in vitro toxicity studies and represents an interesting lead for drug development.

Attenuation of intestinal absorption by major efflux transporters: quantitative tools and strategies using a Caco-2 model.

Efflux transporters expressed in the apical membrane of intestinal enterocytes have been implicated in drug oral absorption. The current study presents a strategy and tools to quantitatively predict the impact of efflux on oral absorption for new chemical entities (NCEs) in early drug discovery. Sixty-three marketed drugs with human absorption data were evaluated in the Caco-2 bidirectional permeability assay and subjected to specific transporter inhibition. A four-zone graphical model was developed from apparent permeability and efflux ratios to quickly identify compounds whose efflux activity may distinctly influence human absorption. NCEs in "zone 4" will probably have efflux as a barrier for oral absorption and further mechanistic studies are required. To interpret mechanistic results, we introduced a new quantitative substrate classification parameter, transporter substrate index (TSI). TSI allowed more flexibility and considered both in vitro and in vivo outcomes. Its application ranged from addressing the challenge of overlapping substrate specificity to projecting the role of transporter(s) on exposure or potential drug-drug interaction risk. The potential impact of efflux transporters associated with physicochemical properties on drug absorption is discussed in the context of TSI and also the previously reported absorption quotient. In this way, the chemistry strategy may be differentially focused on passive permeability or efflux activity or both.

Towards prediction of in vivo intestinal absorption using a 96-well Caco-2 assay.

We systematically validated a robust 96-well Caco-2 assay via an extended set of 93 marketed drugs with diverse transport mechanisms and quantified by LC/MS/MS, to investigate its predictive utility while dealing with challenging discovery compounds. Utilizing nonlinear fit, the validation led to a good correlation (R(2) = 0.76) between absorptive permeability, log P(app)(A-B), from in vitro Caco-2 assay and reported human fraction of dose absorbed. We observed that paracellular compounds could be flagged by log P(app)(A-B) (<-5.5 cm/s) and physicochemical property space (c log P < 1). Of 8000 Novartis discovery compounds examined 13% were subject to low recovery (<30%). Compound loss was investigated by comparing cell monolayer and artificial membrane, while 0.5% bovine serum albumin (in both donor and acceptor compartments) was utilized to improve recovery. The second focus of this study was to investigate the advantages and limitations of the current Caco-2 assay for predicting in vivo intestinal absorption. Caco-2 measurements for compounds with high aqueous solubility and low in vitro metabolic clearance were compared to 88 in vivo rat bioavailability studies. Despite the challenges posed by discovery compounds with suboptimal physicochemical properties, Caco-2 data successfully projected low intestinal absorption. This platform sets the stage for mechanistically evaluating compounds towards improving in vitro-in vivo correlations.

Mitigating permeability-mediated risks in drug discovery.

IMPORTANCE OF THE FIELD: Adequate permeability is essential for good oral absorption and proper interpretation of pharmacokinetic and pharmacological data. Permeability-limiting absorption and distribution, however, are complex phenomena involving multiple mechanisms and organs ranging from gastro-intestine, liver, kidney to BBB, while the options for pharmaceutical improvement of permeability are quite limited. AREAS COVERED IN THIS REVIEW: In this article, the comprehensive in silico, in vitro and in vivo/ex vivo/in situ tools to assess permeability are reviewed, alongside their advantages and limitations. Several key points relating to the in vivo predictive impact of in vitro permeability tools and the caveats when dealing with challenging discovery compounds were also addressed, using drug discovery cases and statistics in Novartis. WHAT THE READER WILL GAIN: The integrated strategy to properly utilize existing permeability tools for mitigating the permeability-related absorption or safety risks at the different stages of drug discovery and development processes is presented. TAKE HOME MESSAGE: It is critical to utilize the comprehensive in silico, in vitro and in vivo/ex vivo tools to dial out permeability issues in early drug discovery, ensuring the right questions to be addressed using the right tools at the right time.

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.