Spatial omics reveals molecular changes in focal cortical dysplasia type II.

Focal cortical dysplasia (FCD) represents a group of diverse localized cortical lesions that are highly epileptogenic and occur due to abnormal brain development caused by genetic mutations, involving the mammalian target of rapamycin (mTOR). These somatic mutations lead to mosaicism in the affected brain, posing challenges to unravel the direct and indirect functional consequences of these mutations. To comprehensively characterize the impact of mTOR mutations on the brain, we employed here a multimodal approach in a preclinical mouse model of FCD type II (Rheb), focusing on spatial omics techniques to define the proteomic and lipidomic changes. Mass Spectrometry Imaging (MSI) combined with fluorescence imaging and label free proteomics, revealed insight into the brain's lipidome and proteome within the FCD type II affected region in the mouse model. MSI visualized disrupted neuronal migration and differential lipid distribution including a reduction in sulfatides in the FCD type II-affected region, which play a role in brain myelination. MSI-guided laser capture microdissection (LMD) was conducted on FCD type II and control regions, followed by label free proteomics, revealing changes in myelination pathways by oligodendrocytes. Surgical resections of FCD type IIb and postmortem human cortex were analyzed by bulk transcriptomics to unravel the interplay between genetic mutations and molecular changes in FCD type II. Our comparative analysis of protein pathways and enriched Gene Ontology pathways related to myelination in the FCD type II-affected mouse model and human FCD type IIb transcriptomics highlights the animal model's translational value. This dual approach, including mouse model proteomics and human transcriptomics strengthens our understanding of the functional consequences arising from somatic mutations in FCD type II, as well as the identification of pathways that may be used as therapeutic strategies in the future.

Multimodal molecular imaging in drug discovery and development.

In addition to individual imaging techniques, the combination and integration of several imaging techniques, so-called multimodal imaging, can provide large amounts of anatomical, functional, and molecular information accelerating drug discovery and development processes. Imaging technologies aid in understanding the disease mechanism, finding new pharmacological targets, and assessment of new potential drug candidates and treatment response. Here, we describe how different imaging techniques can be used in different phases of drug discovery and development and highlight their strengths, related innovations, and future potential with a focus on the implementation of artificial intelligence (AI) and radiomics for imaging technologies.

Setup of human liver-chips integrating 3D models, microwells and a standardized microfluidic platform as proof-of-concept study to support drug evaluation.

Human 3D liver microtissues/spheroids are powerful in vitro models to study drug-induced liver injury (DILI) but the small number of cells per spheroid limits the models' usefulness to study drug metabolism. In this work, we scale up the number of spheroids on both a plate and a standardized organ-chip platform by factor 100 using a basic method which requires only limited technical expertise. We successfully generated up to 100 spheroids using polymer-coated microwells in a 96-well plate (= liver-plate) or organ-chip (= liver-chip). Liver-chips display a comparable cellular CYP3A4 activity, viability, and biomarker expression as liver spheroids for at least one week, while liver-plate cultures display an overall reduced hepatic functionality. To prove its applicability to drug discovery and development, the liver-chip was used to test selected reference compounds. The test system could discriminate toxicity of the DILI-positive compound tolcapone from its less hepatotoxic structural analogue entacapone, using biochemical and morphological readouts. Following incubation with diclofenac, the liver-chips had an increased metabolite formation compared to standard spheroid cultures. In summary, we generated a human liver-chip model using a standardized organ-chip platform which combines up to 100 spheroids and can be used for the evaluation of both drug safety and metabolism.

Prediction of Chameleonic Efficiency.

Chameleonic properties, i. e., the capacity of a molecule to hide polarity in non-polar environments and expose it in water, help achieving sufficient permeability and solubility for drug molecules with high MW. We present models of experimental measures of polarity for a set of 24 FDA approved drugs (MW 405-1113) and one PROTAC (MW 1034). Conformational ensembles in aqueous and non-polar environments were generated using molecular dynamics. A linear regression model that predicts chromatographic apparent polarity (EPSA) with a mean unsigned error of 10 Å2 was derived based on separate terms for donor, acceptor, and total molecular SASA. A good correlation (R2 =0.92) with an experimental measure of hydrogen bond donor potential, Δlog Poct-tol , was found for the mean hydrogen bond donor SASA of the conformational ensemble scaled with Abraham's A hydrogen bond acidity. Two quantitative measures of chameleonic behaviour, the chameleonic efficiency indices, are introduced. We envision that the methods presented herein will be useful to triage designed molecules and prioritize those with the best chance of achieving acceptable permeability and solubility.

Estimating human ADME properties, pharmacokinetic parameters and likely clinical dose in drug discovery.

Introduction: Prediction of human absorption, distribution, metabolism, and excretion (ADME) properties, therapeutic dose and exposure has become an integral part of compound optimization in discovery. Incorporation of drug metabolism and pharmacokinetics into discovery projects has largely tempered historical drug failure due to sub-optimal ADME. In the current era, inadequate safety and efficacy are leading culprits for attrition; both of which are dependent upon drug exposure. Therefore, prediction of human pharmacokinetics (PK) and dose are core components of de-risking strategies in discovery. Areas covered: The authors provide an overview of human dose prediction methods and present a toolbox of PK parameter prediction models with a proposed framework for a consensus approach valid throughout the discovery value chain. Mechanistic considerations and indicators for their application are discussed which may impact the dose prediction approach. Examples are provided to illustrate how implementation of the proposed strategy throughout discovery can assist project progression. Expert opinion: Anticipation of human ADME, therapeutic dose and exposure must be deliberated throughout drug discovery from virtual/initial synthesis where key properties are considered and similar molecules ranked, into development where advanced compounds can be subject to prediction with greater mechanistic understanding and data-driven model selection.

Development and Characterization of a Human Hepatocyte Low Intrinsic Clearance Assay for Use in Drug Discovery.

Progression of new chemical entities is a multiparametric process involving a balance of potency; absorption, distribution, metabolism, and excretion; and safety properties. To accurately predict human pharmacokinetics and estimate human efficacious dose, the use of in vitro measures of clearance is often essential. Low metabolic clearance is often targeted to facilitate in vivo exposure and achieve appropriate half-life. Suspension primary human hepatocytes (PHHs) have been successfully used in predictions of clearance. However, incubation times are limited, hindering the limit of quantification. The aims herein were to evaluate the ability of a novel PHH media supplement, HepExtend, in order to maintain cell function, increase culture times, and define the clearance of stable compounds. Cell activity was analyzed with a range of cytochrome P450 (P450) and UDP-glucuronosyltransferase (UGT) substrates, and the mRNA expression of drug disposition and toxicity marker genes was determined. HepExtend and Geltrex were essential to maintain cell activity and viability for 5 days (N = 3 donors). In comparison with CM4000 ± Geltrex, HepExtend + Geltrex displayed a higher level of gene expression on day 1, particularly for the P450s, nuclear receptors, and UGTs. The novel medium, HepExtend + Geltrex, was robust and reproducible in generating statistically significant intrinsic clearance values at 0.1 µl/min/106 cells over a 30-hour period (P < 0.05), which was lower than previously demonstrated. Following regression correction, human hepatic in vivo clearance was predicted within 3-fold for 83% of compounds tested for three human donors, with an average fold error of 2.2. The novel PHH medium, HepExtend, with matrix overlay offers significant improvement in determining compounds with low intrinsic clearance when compared with alternative approaches.

Bioanalysis of Pseudomonas aeruginosa alkyl quinolone signalling molecules in infected mouse tissue using LC-MS/MS; and its application to a pharmacodynamic evaluation of MvfR inhibition.

Alkyl quinolone molecules 2-heptyl-4-quinolone (HHQ) and 2-heptyl-3-hydroxy-4(1H)-quinolone (PQS) are important quorum sensing signals, which play a mediatory role in the pathogenesis of acute and chronic Pseudomonas aeruginosa infection. A targeted approach inhibiting the bacterial 'multiple virulence factor regulon' (MvfR) protein complex, offers the possibility to block the synthesis of MvfR-dependant signal molecules. Here, a high throughput bioanalytical method was developed using LC-MS/MS detection for the selective determination of HHQ and PQS in mouse tissue homogenate, over a sensitive range of 1-5000 and 10-5000pg/mL, respectively. Chromatographic peak distortion of the iron chelator PQS was overcome with the applied use of a bidentate chelator mobile phase additive 2-Picolinic acid at 0.2mM concentration, giving an improved separation and response for the analyte, whilst maintaining overall MS system robustness. Following thigh infection with P. aeruginosa strain 2-PA14 in mice, the concentration and time course of HHQ and PQS (4-hydroxy-2-alkyl-quinolone (HAQ) biomarkers) residing in the biophase were evaluated, and exhibited a low level combined with a substantial inter-individual variability. Quantifiable levels could be obtained from approximately 15h post infection, to the study termination at 21-22h. A dose dependant reduction in HAQ tissue concentrations at selected time points were obtained following MvfR inhibitor administration versus drug vehicle (p<0.01, Kruskal-Wallis-one way ANOVA) and meta -analyses of several studies enabled an inhibitory concentration (IC50) of 80nM free drug to be determined. However, due to the experimental limitations a defined time profile for in-vivo HAQ production could not be characterised. Microsomal stability measurements demonstrated a rapid metabolic clearance of both alkyl quinolone biomarkers in the bacterial host, with a hepatic extraction ratio greater than 0.96 (the measurable assay limit). High clearance underpinned the low concentrations present in the well-perfused thigh tissue. Along with method development and validation details, this paper considers the kinetics of in-vivo HAQ bio-synthesis during Pseudomonas infection; and risks of biomarker over-estimation from samples which contain an exogenous population of bacteria.

A new paradigm for navigating compound property related drug attrition.

Improving the efficiency of drug discovery remains a major focus for the pharmaceutical industry. Toxicity accounts for 90% of withdrawals and major early-stage terminations relate to suboptimal efficacy and safety. Traditional oral drug space is well defined with respect to physicochemical properties and ADMET risks but increased focus on ligand-lipophilicity efficiency, maximizing enthalpy contributions and new target classes challenge this paradigm. A hybrid space has been identified that combines physical properties and key interactions attributable to drug transporters. A novel algorithm is proposed that incorporates drug-transporter interactions and its utility evaluated against popular ligand efficiency indices. Simply reducing the bulk properties of compounds can exchange one problem for another and creates high-risk areas that challenge the successful delivery from a balanced portfolio.

Application of an in vitro OAT assay in drug design and optimization of renal clearance.

1. Optimization of renal clearance is a complex balance between passive and active processes mediated by renal transporters. This work aimed to characterize the interaction of a series of compounds with rat and human organic anion transporters (OATs) and develop quantitative structure-activity relationships (QSARs) to optimize renal clearance. 2. In vitro inhibition assays were established for human OAT1 and rat Oat3 and rat in vivo renal clearance was obtained. Statistically significant quantitative relationships were explored between the compounds' physical properties, their affinity for OAT1 and oat3 and the inter-relationship with unbound renal clearance (URC) in rat. 3. Many of the compounds were actively secreted and in vitro analysis demonstrated that these were ligands for rat and human OAT transporters (IC50 values ranging from <1 to >100 µM). Application of resultant QSAR models reduced renal clearance in the rat from 24 to <0.1 ml/min/kg. Data analysis indicated that the properties associated with increasing affinity at OATs are the same as those associated with reducing URC but orthogonal in nature. 4. This study has demonstrated that OAT inhibition data and QSAR models can be successfully used to optimize rat renal clearance in vivo and provide confidence of translation to humans.

In silico physicochemical parameter predictions.

Drug discovery is a complex process with the aim of discovering efficacious molecules where their potency and selectivity are balanced against ADMET properties to set the appropriate dose and dosing interval. The link between physicochemical properties and molecular structure are well established. The subsequent connections between physicochemical properties and a drug's biological behavior provide an indirect link back to structure, facilitating the prediction of a biological property as a consequence of a particular molecular manipulation. Due to this understanding, during early drug discovery in vitro physicochemical property assays are commonly performed to eliminate compounds with properties commensurate with high attrition risks. However, the goal is to accurately predict physicochemical properties to prevent the synthesis of high risk compounds and hence minimize wasted drug discovery efforts. This paper will review the relevance to ADMET behaviors of key physicochemical properties, such as ionization, aqueous solubility, hydrogen bonding strength and hydrophobicity, and the in silico methodology for predicting them.

Application of in silico, in vitro and preclinical pharmacokinetic data for the effective and efficient prediction of human pharmacokinetics.

In the present age of pharmaceutical research and development, focused delivery of decision making data is more imperative than ever before. Resulting from several years' success, failure and consequential learning, this article also proffers advice and guidance on which in vitro and in vivo experiments to perform to facilitate efficient and cost-effective pursuit of candidate drugs with acceptable human pharmacokinetic profiles. Predictive in silico models are important in directing design toward compounds with the highest probability of having suitable DMPK properties rather than in predicting human pharmacokinetics per se, and the value and utility of such approaches are reviewed with the intention of providing direction to DMPK scientists. Relating to absorption, distribution, elimination and effective half-life, strategies are described to provide direction in commonly encountered scenarios.

The discovery of CCR3/H1 dual antagonists with reduced hERG risk.

A series of dual CCR3/H(1) antagonists based on a bispiperidine scaffold were discovered. Introduction of an acidic group overcame hERG liability. Bioavailability was optimised by modulation of physico-chemical properties and physical form to deliver a compound suitable for clinical evaluation.

Scaffold-hopping with zwitterionic CCR3 antagonists: identification and optimisation of a series with good potency and pharmacokinetics leading to the discovery of AZ12436092.

The discovery and optimisation of a series of zwitterionic CCR3 antagonists is described. Optimisation of the structure led to AZ12436092, a compound with excellent selectivity over activity at hERG and outstanding pharmacokinetics in preclinical species.

The development, characterization, and application of an OATP1B1 inhibition assay in drug discovery.

The pivotal role of organic anion-transporting polypeptide 1B1 (OATP1B1) in drug disposition has become clear over the last decade. Therefore, an OATP1B1 inhibition assay suitable for use within early drug discovery was developed and characterized. IC(50) estimates for 10 literature compounds using pitavastatin and estradiol-17ß-glucuronide as substrates were within 2-fold of each other. In addition, the IC(50) estimates using pitavastatin uptake agreed well with literature values (r(2) = 0.92, average fold error = 1.3). However, when estrone-3-sulfate was used, OATP1B1 inhibition was underpredicted by as much as 10-fold. A comparison of uptake in human hepatocytes and OATP1B1 inhibition showed a significant correlation (r(2) = 0.53, P < 0.001) for more than 40 compounds. These data suggest that, for discrete chemical series, OATP1B1 inhibition data may be used as a surrogate for more costly and time-consuming uptake studies in hepatocytes. OATP1B1 inhibition data, determined for over 260 compounds representing both internal AstraZeneca and literature chemistry, were also used to generate a continuous in silico model. The robustness of the model was demonstrated by accurately predicting OATP1B1 inhibition for external test sets using 50 AstraZeneca compounds (root mean square error = 0.45) and 12 literature drugs (RMSE = 0.32). The most important molecular descriptors for the prediction of OATP1B1 inhibition were maximal hydrogen bonding strength followed by cLogP. This study has shown that a well validated OATP1B1 inhibition assay in conjunction with an in silico approaches has the potential to influence significantly the design-make-test cycle and subsequently reduce the propensity of OATP1B1 ligands.

A highly automated assay for determining the aqueous equilibrium solubility of drug discovery compounds.

Aqueous solubility is an important physicochemical parameter for any potential drug candidate, and high-throughput kinetic assays are frequently used in drug discovery to give an estimate of a compound's aqueous solubility. However, the aqueous solubility data from an equilibrium (thermodynamic) shake-flask technique is considered more relevant, but is slower and more labor intensive to generate. A highly automated aqueous equilibrium solubility shake-flask technique is described and validated on a set of 15 marketed drugs, whose aqueous solubilities cover four orders of magnitude. The assay uses a Tecan Freedom Evo 200 liquid handling robot (Tecan Group Ltd., Männerdorf, Switzerland) with integrated appliances for the transportation, decapping and recapping, and centrifugation of sample tubes. These bespoke automation solutions help overcome the labor intensive steps associated with performing conventional, gold standard, aqueous equilibrium solubility shake-flask measurements, enabling the assay to be used as a primary-wave drug discovery screen.

Lipophilicity of acidic compounds: impact of ion pair partitioning on drug design.

In drug discovery projects the ability to show a relationship between a compound's molecular structure and its pharmacokinetic, in vivo efficacy, or toxicity profile is paramount for the design of better analogues. To aid this understanding the measurement of distribution coefficients at some physiologically relevant pH, for example, log D(7.4), is common practice as they are used as a key descriptor in mathematical models for predicting various biological parameters. Evidence is presented that under typical experimental conditions ion pair partitioning can contribute greatly to log D(7.4) results for acidic compounds; if this is ignored it may compromise data analysis within drug discovery projects where the modulation of lipophilicity is a primary design strategy. The work herein focuses on acidic compounds and reflects the experience of AstraZeneca R&D Charnwood (AZ) where ion pair partitioning contributions can be minimized by the routine measurement of log D(5.5) data. The magnitude of ion pair partitioning contributions to the log D(7.4) measurements of 24 acidic drugs are investigated, and the risks to drug discovery projects that ignore such contributions are discussed. The superiority of measured lipophilicity data over calculated data for a set of AZ proprietary acidic compounds is also presented.

A kinetic method for the determination of plasma protein binding of compounds unstable in plasma: Specific application to enalapril.

Traditional methods for the determination of plasma protein binding (PPB), such as equilibrium dialysis and ultrafiltration, normally operate on a timescale ranging from tens of minutes to several hours and are not suitable for measuring compounds that have significant chemical degradation on this timescale. One such compound is enalapril. Although stable in human plasma enalapril is subject to rapid esterase-catalyzed hydrolysis in rat plasma. A method has been developed which allows the extent of rat PPB of enalapril to be determined from initial rates kinetics of the adsorption of the unstable compound to dextran coated charcoal (DCC). The method has been applied to stable compounds, and the results are consistent with those from traditional equilibrium dialysis experiments. The experimental method is simple to run, requires no specialized equipment, and can potentially be applied to other compounds that show instability in plasma where traditional experimental techniques are unsuitable.

A method for measuring the lipophilicity of compounds in mixtures of 10.

Lipophilicity is an important parameter for any potential drug candidate. Accurate and efficient lipophilicity measurements facilitate the development of high-quality predictive in silico models that support the design of future drugs. Lipophilicity estimates derived from the traditional 1-octanol/water shake flask techniques have been the most widely employed and are therefore the best understood. This technique can be considered to give a good measure of a compound's lipophilicity, albeit slower and more labor intensive to run compared with some other methodologies. Herein is described and validated an efficient 1-octanol/water shake flask technique that has sufficient capacity to be run as a primary screen within the drug discovery process. This is achieved by the simultaneous measurement of the distribution coefficients of mixtures of up to 10 compounds using high-performance liquid chromatography and tandem mass spectrometry. Concerns regarding ion pair partitioning that could result in erroneous results due to interactions between compounds within a mixture are discussed.

A rapid computational filter for predicting the rate of human renal clearance.

In silico models that predict the rate of human renal clearance for a diverse set of drugs, that exhibit both active secretion and net re-absorption, have been produced using three statistical approaches. Partial Least Squares (PLS) and Random Forests (RF) have been used to produce continuous models whereas Classification And Regression Trees (CART) has only been used for a classification model. The best models generated from either PLS or RF produce significant models that can predict acids/zwitterions, bases and neutrals with approximate average fold errors of 3, 3 and 4, respectively, for an independent test set that covers oral drug-like property space. These models contain additional information on top of any influence arising from plasma protein binding on the rate of renal clearance. Classification And Regression Trees (CART) has been used to generate a classification tree leading to a simple set of Renal Clearance Rules (RCR) that can be applied to man. The rules are influenced by lipophilicity and ion class and can correctly predict 60% of an independent test set. These percentages increase to 71% and 79% for drugs with renal clearances of < 0.1 ml/min/kg and > 1 ml/min/kg, respectively. As far as the authors are aware these are the first set of models to appear in the literature that predict the rate of human renal clearance and can be used to manipulate molecular properties leading to new drugs that are less likely to fail due to renal clearance.

From ATP to AZD6140: the discovery of an orally active reversible P2Y12 receptor antagonist for the prevention of thrombosis.

Starting from adenosine triphosphate (ATP), the identification of a novel series of P2Y(12) receptor antagonists and exploitation of their SAR is described. Modifications of the acidic side chain and the purine core and investigation of hydrophobic substituents led to a series of neutral molecules. The leading compound, 17 (AZD6140), is currently in a large phase III clinical trial for the treatment of acute coronary syndromes and prevention of thromboembolic clinical sequelae.