Toward reproducible tumor organoid culture: focusing on primary liver cancer.

Organoids present substantial potential for pushing forward preclinical research and personalized medicine by accurately recapitulating tissue and tumor heterogeneity in vitro. However, the lack of standardized protocols for cancer organoid culture has hindered reproducibility. This paper comprehensively reviews the current challenges associated with cancer organoid culture and highlights recent multidisciplinary advancements in the field with a specific focus on standardizing liver cancer organoid culture. We discuss the non-standardized aspects, including tissue sources, processing techniques, medium formulations, and matrix materials, that contribute to technical variability. Furthermore, we emphasize the need to establish reproducible platforms that accurately preserve the genetic, proteomic, morphological, and pharmacotypic features of the parent tumor. At the end of each section, our focus shifts to organoid culture standardization in primary liver cancer. By addressing these challenges, we can enhance the reproducibility and clinical translation of cancer organoid systems, enabling their potential applications in precision medicine, drug screening, and preclinical research.

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.

BACKGROUND: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online platforms based on deep learning for drug-target interaction, affinity, and binding sites identification, there is currently no integrated online platforms for all three aspects. RESULTS: Our solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of "interaction-affinity-binding sites". Drug-Online platform consists of three parts: the first part uses the drug-target interaction identification method MGraphDTA, based on graph neural networks (GNN) and convolutional neural networks (CNN), to identify whether there is a drug-target interaction. If an interaction is identified, the second part employs the drug-target affinity identification method MMDTA, also based on GNN and CNN, to calculate the strength of drug-target interaction, i.e., affinity. Finally, the third part identifies drug-target binding sites, i.e., pockets. The method pt-lm-gnn used in this part is also based on GNN. CONCLUSIONS: Drug-Online is a reliable online platform that integrates drug-target interaction, affinity, and binding sites identification. It is freely available via the Internet at http://39.106.7.26:8000/Drug-Online/ .

Tumor Organoids: The Era of Personalized Medicine.

The strategies of future medicine are aimed to modernize and integrate quality approaches including early molecular-genetic profiling, identification of new therapeutic targets and adapting design for clinical trials, personalized drug screening (PDS) to help predict and individualize patient treatment regimens. In the past decade, organoid models have emerged as an innovative in vitro platform with the potential to realize the concept of patient-centered medicine. Organoids are spatially restricted three-dimensional clusters of cells ex vivo that self-organize into complex functional structures through genetically programmed determination, which is crucial for reconstructing the architecture of the primary tissue and organs. Currently, there are several strategies to create three-dimensional (3D) tumor systems using (i) surgically resected patient tissue (PDTOs, patient-derived tumor organoids) or (ii) single tumor cells circulating in the patient's blood. Successful application of 3D tumor models obtained by co-culturing autologous tumor organoids (PDTOs) and peripheral blood lymphocytes have been demonstrated in a number of studies. Such models simulate a 3D tumor architecture in vivo and contain all cell types characteristic of this tissue, including immune system cells and stem cells. Components of the tumor microenvironment, such as fibroblasts and immune system cells, affect tumor growth and its drug resistance. In this review, we analyzed the evolution of tumor models from two-dimensional (2D) cell cultures and laboratory animals to 3D tissue-specific tumor organoids, their significance in identifying mechanisms of antitumor response and drug resistance, and use of these models in drug screening and development of precision methods in cancer treatment.

Fluid-Dynamic Culture of Tumour and Immune Cells for More Predictive Infiltration Studies and Immunotherapy Drug Screening.

Immunotherapies represent one of the current most promising challenges in cancer treatment. They are based on the boost of natural immune responses, aimed at cancer eradication. However, the success of immunotherapeutic approaches strictly depends on the interaction between immune cells and cancer cells. Preclinical drug tests currently available are poor in fully predicting the actual safety and efficacy of immunotherapeutic treatments under development. Indeed, conventional 2D cell culture underrepresents the complexity of the tumour microenvironment, while in vivo animal models lack in mimicking the human immune cell responses. In this context, predictability, reliability, and complete immune compatibility still represent challenges to overcome. For this aim, novel 3D, fully humanized in vitro cancer tissue models have been recently optimized by adopting emerging technologies, such as organ-on-chips (OOC) and 3D cancer cell-laden hydrogels. In particular, a novel multi-in vitro organ (MIVO) OOC platform has been recently adopted to culture 3D clinically relevant size cancer tissues under proper physiological culture conditions to investigate anti-cancer treatments and immune-tumour cell crosstalk.The proposed immune-tumour OOC-based model offers a potential tool for accurately modelling human immune-related diseases and effectively assessing immunotherapy efficacy, finally offering promising experimental approaches for personalized medicine.

Analysis of post-market adverse events of istradefylline: a real-world study base on FAERS database.

Analyze the adverse event (AE) signals of istradefylline based on the FAERS database. By extracting large-scale data from the FAERS database, this study used various signal quantification techniques such as ROR, PRR, BCPNN, and MGPS to calculate and evaluate the ratio and association between istradefylline and specific AEs. In the FAERS database, this study extracted data from the third quarter of 2019 to the first quarter of 2023, totaling 6,749,750 AE reports. After data cleansing and drug screening, a total of 3633 AE reports related to istradefylline were included for analysis. Based on four calculation methods, this study unearthed 25 System Organ Class (SOC) AE signals and 82 potential preferred terms (PTs) related to istradefylline. The analysis revealed new AEs during istradefylline treatment, including reports of Parkinsonism hyperpyrexia syndrome (n = 3, ROR 178.70, PRR 178.63, IC 1.97, EBGM 165.63), Compulsions (n = 5, ROR 130.12, PRR 130.04, IC 2.53, EBGM 123.02), Deep brain stimulation (n = 10, ROR 114.42, PRR 114.27, IC 3.33, EBGM 108.83), and Freezing phenomenon (n = 60, ROR 97.52, PRR 96.76, IC 5.21, EBGM 92.83). This study provides new risk signals and important insights into the use of istradefylline, but further research and validation are needed, especially for those AE that may occur in actual usage scenarios but are not yet explicitly described in the instructions.

Screening for Forensically Relevant Drugs Using Data-Independent High-Resolution Mass Spectrometry.

Forensic and clinical laboratories are expected to provide a rapid screening of samples for a wide range of analytes; however, the ever-changing landscape of illicit substances makes analysis complicated. There is a great need for untargeted methods that can aid these laboratories in broad-scope drug screening. Liquid chromatography hyphenated with high-resolution mass spectrometry (LC-HRMS) has become a popular technique for untargeted screening and presumptive identification of drugs of abuse due to its superior sensitivity and detection capabilities in complex matrices. An untargeted extraction and data acquisition method was evaluated for the broad screening of high-priority drugs of abuse in whole blood. A total of 35 forensically relevant target analytes were identified and extracted at biologically relevant low and high (10× low) concentrations from whole blood using supported liquid extraction. Data-independent acquisition was accomplished using ultraperformance liquid chromatography and a quadrupole time-of-flight mass spectrometry. Results were acceptable for screening assays, with limits of detection at or below the recommended low-concentration cutoffs for most analytes. Analyte ionization varied from 30.1 to 267.6% (average: 110.5%) at low concentrations and from 8.6 to 383.5% (average: 93.6%) at high concentrations. Extraction recovery ranged from 8.5 to 330.5% (average: 105.3%) at low concentrations and from 9.4 to 127.5% (average: 82.7%) at high concentrations. This variability was also captured as precision, ranging from 4.7 to 135.2% (average: 36.5%) at low concentrations and from 0.9 to 59.0% (average: 21.7%) at high concentrations. The method described in this work is efficient and effective for qualitative forensic toxicology screening, as demonstrated by analysis of 166 authentic suspected impaired driver and postmortem specimens. That said, it is critical that laboratories establishing untargeted LC-HRMS screening assays be aware of the strengths and limitations across diverse drug categories and chemical structures.

Cell viability and cytotoxicity assays: Biochemical elements and cellular compartments.

Cell viability and cytotoxicity assays play a crucial role in drug screening and evaluating the cytotoxic effects of various chemicals. The quantification of cell viability and proliferation serves as the cornerstone for numerous in vitro assays that assess cellular responses to external factors. In the last decade, several studies have developed guidelines for defining and interpreting cell viability and cytotoxicity based on morphological, biochemical, and functional perspectives. As this domain continues to experience ongoing growth, revealing new mechanisms orchestrating diverse cell cytotoxicity pathways, we suggest a revised classification for multiple assays employed in evaluating cell viability and cell death. This classification is rooted in the cellular compartment and/or biochemical element involved, with a specific focus on mechanistic and essential aspects of the process. The assays are founded on diverse cell functions, encompassing metabolic activity, enzyme activity, cell membrane permeability and integrity, adenosine 5'-triphosphate content, cell adherence, reduction equivalents, dye inclusion or exclusion, constitutive protease activity, colony formation, DNA fragmentation and nuclear splitting. These assays present straightforward, reliable, sensitive, reproducible, cost-effective, and high-throughput approaches for appraising the effects of newly formulated chemotherapeutic biomolecules on the cell survival during the drug development process.

Development of a rapid-fire drug screening method by probe electrospray ionization tandem mass spectrometry for human urine (RaDPi-U).

Drug screening tests are mandatory in the search for drugs in forensic biological samples, and immunological methods and mass spectrometry (e.g., gas chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry) are commonly used for that purpose. However, these methods have some drawbacks, and developing new screening methods is required. In this study, we develop a rapid-fire drug screening method by probe electrospray ionization tandem mass spectrometry (PESI-MS/MS), which is an ambient ionization mass spectrometry method, for human urine, named RaDPi-U. RaDPi-U is carried out in three steps: (1) mixing urine with internal standard (IS) solution and ethanol, followed by vortexing; (2) pipetting the mixture onto a sample plate for PESI; and (3) rapid-fire analysis by PESI-MS/MS. RaDPi-U targets 40 forensically important drugs, which include illegal drugs, hypnotics, and psychoactive substances. The analytical results were obtained within 3 min because of the above-mentioned simple workflow of RaDPi-U. The calibration curves of each analyte were constructed using the IS method, and they were quantitatively valid, resulting in good linearity (0.972-0.999) with a satisfactory lower limit of detection and lower limit of quantitation (0.01-7.1 ng/mL and 0.02-21 ng/mL, respectively). Further, both trueness and precisions were 28% or less, demonstrating the high reliability and repeatability of the method. Finally, we applied RaDPi-U to three postmortem urine specimens and successfully detected different drugs in each urine sample. The practicality of the method is proven, and RaDPi-U will be a strong tool as a rapid-fire drug screening method not only in forensic toxicology but also in clinical toxicology.

Prediction of protein-ligand binding affinity via deep learning models.

Accurately predicting the binding affinity between proteins and ligands is crucial in drug screening and optimization, but it is still a challenge in computer-aided drug design. The recent success of AlphaFold2 in predicting protein structures has brought new hope for deep learning (DL) models to accurately predict protein-ligand binding affinity. However, the current DL models still face limitations due to the low-quality database, inaccurate input representation and inappropriate model architecture. In this work, we review the computational methods, specifically DL-based models, used to predict protein-ligand binding affinity. We start with a brief introduction to protein-ligand binding affinity and the traditional computational methods used to calculate them. We then introduce the basic principles of DL models for predicting protein-ligand binding affinity. Next, we review the commonly used databases, input representations and DL models in this field. Finally, we discuss the potential challenges and future work in accurately predicting protein-ligand binding affinity via DL models.

Detection of biomagnetic signals from induced pluripotent stem cell-derived cardiomyocytes using deep learning with simulation data.

The detection of spontaneous magnetic signals can be used for the non-invasive electrophysiological evaluation of induced pluripotent stem cell-derived cardiomyocytes (iPS-CMs). We report that deep learning with a dataset that combines magnetic signals estimated using numerical simulation and actual noise data is effective in the detection of weak biomagnetic signals. To verify the feasibility of this method, we measured artificially generated magnetic signals that mimic cellular magnetic fields using a superconducting quantum interference device and attempted peak detection using a long short-term memory network. We correctly detected 80.0% of the peaks and the method achieved superior detection performance compared with conventional methods. Next, we attempted peak detection for magnetic signals measured from mouse iPS-CMs. The number of detected peaks was consistent with the spontaneous beats counted using microscopic observation and the average peak waveform achieved good similarity with the prediction. We also observed the synchronization of peak positions between simultaneously measured field potentials and magnetic signals. Furthermore, the magnetic measurements of cell samples treated with isoproterenol showed potential for the detection of chronotropic effects. These results suggest that the proposed method is effective and has potential application in the safety assessment of regenerative medicine and drug screening.

Review: 3D cell models for organ-on-a-chip applications.

Two-dimensional (2D) cultures do not fully reflect the human organs' physiology and the real effectiveness of the used therapy. Therefore, three-dimensional (3D) models are increasingly used in bioanalytical science. Organ-on-a-chip systems are used to obtain cellular in vitro models, better reflecting the human body's in vivo characteristics and allowing us to obtain more reliable results than standard preclinical models. Such 3D models can be used to understand the behavior of tissues/organs in response to selected biophysical and biochemical factors, pathological conditions (the mechanisms of their formation), drug screening, or inter-organ interactions. This review characterizes 3D models obtained in microfluidic systems. These include spheroids/aggregates, hydrogel cultures, multilayers, organoids, or cultures on biomaterials. Next, the methods of formation of different 3D cultures in Organ-on-a-chip systems are presented, and examples of such Organ-on-a-chip systems are discussed. Finally, current applications of 3D cell-on-a-chip systems and future perspectives are covered.

Exploring the mechanism of contact-dependent cell-cell communication on chemosensitivity based on single-cell high-throughput drug screening platform.

High-throughput drug screening (HTDS) has significantly reduced the time and cost of new drug development. Nonetheless, contact-dependent cell-cell communication (CDCCC) may impact the chemosensitivity of tumour cells. There is a pressing need for low-cost single-cell HTDS platforms, alongside a deep comprehension of the mechanisms by which CDCCC affects drug efficacy, to fully unveil the efficacy of anticancer drugs. In this study, we develop a microfluidic chip for single-cell HTDS and evaluate the molecular mechanisms impacted by CDCCC using quantitative mass spectrometry-based proteomics. The chip achieves high-quality drug mixing and single-cell capture, with single-cell drug screening results on the chip showing consistency with those on the 96-well plates under varying concentration gradients. Through quantitative proteomic analysis, we deduce that the absence of CDCCC in single tumour cells can enhance their chemoresistance potential, but simultaneously subject them to stronger proliferation inhibition. Additionally, pathway enrichment analysis suggests that CDCCC could impact several signalling pathways in tumour single cells that regulate vital biological processes such as tumour proliferation, adhesion, and invasion. These results offer valuable insights into the potential connection between CDCCC and the chemosensitivity of tumour cells. This research paves the way for the development of single-cell HTDC platforms and holds the promise of advancing tumour personalized treatment strategies.

Advancing preclinical drug evaluation through automated 3D imaging for high-throughput screening with kidney organoids.

High-throughput drug screening is crucial for advancing healthcare through drug discovery. However, a significant limitation arises from availablein vitromodels using conventional 2D cell culture, which lack the proper phenotypes and architectures observed in three-dimensional (3D) tissues. Recent advancements in stem cell biology have facilitated the generation of organoids-3D tissue constructs that mimic human organsin vitro. Kidney organoids, derived from human pluripotent stem cells, represent a significant breakthrough in disease representation. They encompass major kidney cell types organized within distinct nephron segments, surrounded by stroma and endothelial cells. This tissue allows for the assessment of structural alterations such as nephron loss, a characteristic of chronic kidney disease. Despite these advantages, the complexity of 3D structures has hindered the use of organoids for large-scale drug screening, and the drug screening pipelines utilizing these complexin vitromodels remain to be established for high-throughput screening. In this study, we address the technical limitations of kidney organoids through fully automated 3D imaging, aided by a machine-learning approach for automatic profiling of nephron segment-specific epithelial morphometry. Kidney organoids were exposed to the nephrotoxic agent cisplatin to model severe acute kidney injury. An U.S. Food and Drug Administration (FDA)-approved drug library was tested for therapeutic and nephrotoxicity screening. The fully automated pipeline of 3D image acquisition and analysis identified nephrotoxic or therapeutic drugs during cisplatin chemotherapy. The nephrotoxic potential of these drugs aligned with previousin vivoand human reports. Additionally, Imatinib, a tyrosine kinase inhibitor used in hematological malignancies, was identified as a potential preventive therapy for cisplatin-induced kidney injury. Our proof-of-concept report demonstrates that the automated screening process, using 3D morphometric assays with kidney organoids, enables high-throughput screening for nephrotoxicity and therapeutic assessment in 3D tissue constructs.

High-throughput 3D microfluidic chip for generation of concentration gradients and mixture combinations.

Concentration gradient generation and mixed combinations of multiple solutions are of great value in the field of biomedical research. However, existing concentration gradient generators for single or two-drug solutions cannot simultaneously achieve multiple concentration gradient formations and mixed solution combinations. Furthermore, the whole system was huge, and required expensive auxiliary equipment, which may lead to complex operations. To address this problem, we devised a novel 3D microchannel network design, which is capable of creating all the desired mixture combinations and concentration gradients of given small amounts of the input solutions. As a proof of concept, the device we presented was verified by both colorimetric and fluorescence detection methods to test the efficiency. This can enable the implementation of one to three solutions with no driving pump and facilitate unique multiple types of more concentration gradients and mixture combinations in a single operation. We envision that this will be a promising candidate for the development of simplified methods for screening of the appropriate concentration and combination, such as various drug screening applications.

GraphormerDTI: A graph transformer-based approach for drug-target interaction prediction.

The application of Artificial Intelligence (AI) to screen drug molecules with potential therapeutic effects has revolutionized the drug discovery process, with significantly lower economic cost and time consumption than the traditional drug discovery pipeline. With the great power of AI, it is possible to rapidly search the vast chemical space for potential drug-target interactions (DTIs) between candidate drug molecules and disease protein targets. However, only a small proportion of molecules have labelled DTIs, consequently limiting the performance of AI-based drug screening. To solve this problem, a machine learning-based approach with great ability to generalize DTI prediction across molecules is desirable. Many existing machine learning approaches for DTI identification failed to exploit the full information with respect to the topological structures of candidate molecules. To develop a better approach for DTI prediction, we propose GraphormerDTI, which employs the powerful Graph Transformer neural network to model molecular structures. GraphormerDTI embeds molecular graphs into vector-format representations through iterative Transformer-based message passing, which encodes molecules' structural characteristics by node centrality encoding, node spatial encoding and edge encoding. With a strong structural inductive bias, the proposed GraphormerDTI approach can effectively infer informative representations for out-of-sample molecules and as such, it is capable of predicting DTIs across molecules with an exceptional performance. GraphormerDTI integrates the Graph Transformer neural network with a 1-dimensional Convolutional Neural Network (1D-CNN) to extract the drugs' and target proteins' representations and leverages an attention mechanism to model the interactions between them. To examine GraphormerDTI's performance for DTI prediction, we conduct experiments on three benchmark datasets, where GraphormerDTI achieves a superior performance than five state-of-the-art baselines for out-of-molecule DTI prediction, including GNN-CPI, GNN-PT, DeepEmbedding-DTI, MolTrans and HyperAttentionDTI, and is on a par with the best baseline for transductive DTI prediction. The source codes and datasets are publicly accessible at https://github.com/mengmeng34/GraphormerDTI.

C. elegans: a prominent platform for modeling and drug screening in neurological disorders.

INTRODUCTION: Human neurodevelopmental and neurodegenerative diseases (NDevDs and NDegDs, respectively) encompass a broad spectrum of disorders affecting the nervous system with an increasing incidence. In this context, the nematode C. elegans, has emerged as a benchmark model for biological research, especially in the field of neuroscience. AREAS COVERED: The authors highlight the numerous advantages of this tiny worm as a model for exploring nervous system pathologies and as a platform for drug discovery. There is a particular focus given to describing the existing models of C. elegans for the study of NDevDs and NDegDs. Specifically, the authors underscore their strong applicability in preclinical drug development. Furthermore, they place particular emphasis on detailing the common techniques employed to explore the nervous system in both healthy and diseased states. EXPERT OPINION: Drug discovery constitutes a long and expensive process. The incorporation of invertebrate models, such as C. elegans, stands as an exemplary strategy for mitigating costs and expediting timelines. The utilization of C. elegans as a platform to replicate nervous system pathologies and conduct high-throughput automated assays in the initial phases of drug discovery is pivotal for rendering therapeutic options more attainable and cost-effective.

Discovery of a potent inhibitor, D-132, targeting AsfvPolX, via protein-DNA complex-guided pharmacophore screening and in vitro molecular characterizations.

The heightened transmissibility and capacity of African swine fever virus (ASFV) induce fatal diseases in domestic pigs and wild boars, posing significant economic repercussions and global threats. Despite extensive research efforts, the development of potent vaccines or treatments for ASFV remains a persistent challenge. Recently, inhibiting the AsfvPolX, a key DNA repair enzyme, emerges as a feasible strategy to disrupt viral replication and control ASFV infections. In this study, a comprehensive approach involving pharmacophore-based inhibitor screening, coupled with biochemical and biophysical analyses, were implemented to identify, characterize, and validate potential inhibitors targeting AsfvPolX. The constructed pharmacophore model, Phar-PolX-S, demonstrated efficacy in identifying a potent inhibitor, D-132 (IC50 = 2.8 ± 0.2 µM), disrupting the formation of the AsfvPolX-DNA complex. Notably, D-132 exhibited strong binding to AsfvPolX (KD = 6.9 ± 2.2 µM) through a slow-on-fast-off binding mechanism. Employing molecular modeling, it was elucidated that D-132 predominantly binds in-between the palm and finger domains of AsfvPolX, with crucial residues (R42, N48, Q98, E100, F102, and F116) identified as hotspots for structure-based inhibitor optimization. Distinctively characterized by a 1,2,5,6-tetrathiocane with modifications at the 3 and 8 positions involving ethanesulfonates, D-132 holds considerable promise as a lead compound for the development of innovative agents to combat ASFV infections.

Using birth-death processes to infer tumor subpopulation structure from live-cell imaging drug screening data.

Tumor heterogeneity is a complex and widely recognized trait that poses significant challenges in developing effective cancer therapies. In particular, many tumors harbor a variety of subpopulations with distinct therapeutic response characteristics. Characterizing this heterogeneity by determining the subpopulation structure within a tumor enables more precise and successful treatment strategies. In our prior work, we developed PhenoPop, a computational framework for unravelling the drug-response subpopulation structure within a tumor from bulk high-throughput drug screening data. However, the deterministic nature of the underlying models driving PhenoPop restricts the model fit and the information it can extract from the data. As an advancement, we propose a stochastic model based on the linear birth-death process to address this limitation. Our model can formulate a dynamic variance along the horizon of the experiment so that the model uses more information from the data to provide a more robust estimation. In addition, the newly proposed model can be readily adapted to situations where the experimental data exhibits a positive time correlation. We test our model on simulated data (in silico) and experimental data (in vitro), which supports our argument about its advantages.

Identification of Novel Antimicrobial Compounds Targeting Mycobacterium tuberculosis S-Adenosyl-L-Homocysteine Hydrolase Using Dual Hierarchical In Silico Structure-Based Drug Screening.

The emergence of multidrug-resistant and extensively drug-resistant Mycobacterium tuberculosis (M. tuberculosis) has become a major medical problem. S-adenosyl-L-homocysteine hydrolase (MtSAHH) was selected as the target protein for the identification of novel anti-TB drugs. Dual hierarchical in silico Structure-Based Drug Screening was performed using a 3D compound structure library (with over 150 thousand synthetic chemicals) to identify compounds that bind to MtSAHH's active site. In vitro experiments were conducted to verify whether the nine compounds selected as new drug candidates exhibited growth-inhibitory effects against mycobacteria. Eight of the nine compounds that were predicted by dual hierarchical screening showed growth-inhibitory effects against Mycobacterium smegmatis (M. smegmatis), a model organism for M. tuberculosis. Compound 7 showed the strongest antibacterial activity, with an IC50 value of 30.2 µM. Compound 7 did not inhibit the growth of Gram-negative bacteria or exert toxic effects on human cells. Molecular dynamics simulations of 40 ns using the MtSAHH-Compound 7 complex structure suggested that Compound 7 interacts stably with the MtSAHH active site. These in silico and in vitro results suggested that Compound 7 is a promising lead compound for the development of new anti-TB drugs.

Evaluation of the translation of multiple cardiovascular regulatory mechanisms in the anesthetized dog.

The strategic and targeted use of an anesthetized canine cardiovascular model early in drug discovery enables a comprehensive cardiovascular and electrophysiological assessment of potential safety liabilities and guides compound selection prior to initiation of chronic toxicological studies. An ideal model would enable exposure-response relationships to guide safety margin calculations, have a low threshold to initiate, and have quick delivery of decision quality data. We have aimed to profile compounds with diverse mechanism of actions (MoAs) of "non-QT" cardiovascular drug effects and evaluate the ability of nonclinical in vivo cardiovascular models to detect clinically reported effects. The hemodynamic effects of 11 drugs (atropine, itraconazole, atenolol, ivabradine, milrinone, enalaprilat, fasudil, amlodipine, prazosin, amiloride, and hydrochlorothiazide) were profiled in an anesthetized dog cardiovascular model. Derived parameters included: heart rate, an index of left ventricular contractility, mean arterial pressure, systemic vascular resistance, and cardiac output. Species specific plasma protein data was generated (human, dog) and utilized to calculate free drug concentrations. Using the anesthetized dog cardiovascular model, 10 of the 11 drugs displayed the predicted changes in CV parameters based on their primary MoAs and corresponding clinically described effects. Interestingly but not unexpected, 1 of 11 failed to display their predicted CV pattern which is likely due to a delay in pharmacodynamic effect that is beyond the duration of the experimental model (hydrochlorothiazide). The analysis from the current study supports the strategic use of the anesthetized dog model early in the drug discovery process for a comprehensive cardiovascular evaluation with good translation to human.