Can preclinical drug development help to predict adverse events in clinical trials?

Preclinical toxicity evaluation of the novel anti-hypertensive compound KSD179019

The Secretin receptor (SCTR) presents a promising path for hypertension management, with KSD179019 as identified as a Positive Allosteric Modulator (PAM) of SCTR, demonstrating anti-hypertensive effects in animal models. Our objective was to comprehensively evaluate the potential toxicity of KSD179019 through in vitro and in vivo investigations. Initial in vitro studies showed minimal toxicity in liver and kidney cells and non-mutagenicity in bacterial assays. A 14-day acute toxicity test indicated an LD50 over 5000 mg/kg body weight, suggesting a safe profile, yet necessitating further in vivo analysis before progressing to human trials. Following OECD protocols, we conducted sub-chronic (90 days) and chronic (180 days) toxicity studies in male and female C57 mice at various dosages. These included comprehensive hematological, biochemical, macroscopic, urinalysis, and histopathological examinations. The sub-chronic study reported minimal toxicity except at the highest doses (700 and 1000 mg/kg), while the chronic study suggested a no-observed-adverse-effect-level (NOAEL) at 250 mg/kg with limitations. QSAR analysis supported the non-mutagenic nature of KSD179019. KSD179019 demonstrated a favorable general toxicity profile at a dose of 250 mg/kg in a 180-day chronic testing study. However, further preclinical investigations, including assessments of in vivo mutagenicity, reproductive and developmental toxicity, and carcinogenicity, are required to comprehensively establish its safety profile.

Gap-Δenergy, a New Metric of the Bond Energy State, Assisting to Predict Molecular Toxicity

Molecular toxicity is a critical feature of drug development. It is thus very important to develop computational models to evaluate the toxicity of small molecules. The accuracy of toxicity prediction largely depends on the quality of molecular representation; however, current methods for this purpose do not address this issue well. Here, we introduce a new metric, gap-Δenergy, which is designed to quantify the intermolecular bond energy difference with atom distance. We next find significant variations in the gap-Δenergy distribution among different types of molecules. Moreover, we show that this metric is able to distinguish the toxic small molecules. We collected data sets of toxic and exogenous small molecules and presented a novel index, namely, global toxicity, to evaluate the overall toxicity of molecules. Based on molecular descriptors and the proposed gap-Δenergy metric, we further constructed machine learning models that were trained with 7816 small molecules. The XGBoost-based model achieved the best performance with an AUC score of 0.965 and an F1 score of 0.849 on the test set (1954 small molecules), which outperformed the model that did not use gap-Δenergy features, with a sensitivity score increase of 3.2%.

BiMPADR: A Deep Learning Framework for Predicting Adverse Drug Reactions in New Drugs

Detecting the unintended adverse reactions of drugs (ADRs) is a crucial concern in pharmacological research. The experimental validation of drug-ADR associations often entails expensive and time-consuming investigations. Thus, a computational model to predict ADRs from known associations is essential for enhanced efficiency and cost-effectiveness. Here, we propose BiMPADR, a novel model that integrates drug gene expression into adverse reaction features using a message passing neural network on a bipartite graph of drugs and adverse reactions, leveraging publicly available data. By combining the computed adverse reaction features with the structural fingerprints of drugs, we predict the association between drugs and adverse reactions. Our models obtained high AUC (area under the receiver operating characteristic curve) values ranging from 0.861 to 0.907 in an external drug validation dataset under differential experiment conditions. The case study on multiple BET inhibitors also demonstrated the high accuracy of our predictions, and our model's exploration of potential adverse reactions for HWD-870 has contributed to its research and development for market approval. In summary, our method would provide a promising tool for ADR prediction and drug safety assessment in drug discovery and development.

Structure-property Relationships Reported for the New Drugs Approved in 2022

BACKGROUND

The structure-property relationship illustrates how modifying the chemical structure of a pharmaceutical compound influences its absorption, distribution, metabolism, excretion, and other related properties. Understanding structure-property relationships of clinically approved drugs could provide useful information for drug design and optimization strategies.

METHOD

Among new drugs approved around the world in 2022, including 37 in the US, structure- property relationships of seven drugs were compiled from medicinal chemistry literature, in which detailed pharmacokinetic and/or physicochemical properties were disclosed not only for the final drug but also for its key analogues generated during drug development.

RESULTS

The discovery campaigns for these seven drugs demonstrate extensive design and optimization efforts to identify suitable candidates for clinical development. Several strategies have been successfully employed, such as attaching a solubilizing group, bioisosteric replacement, and deuterium incorporation, resulting in new compounds with enhanced physicochemical and pharmacokinetic properties.

CONCLUSION

The structure-property relationships hereby summarized illustrate how proper structural modifications could successfully improve the overall drug-like properties. The structure-property relationships of clinically approved drugs are expected to continue to provide valuable references and guides for the development of future drugs.

Amazonian medicinal plants efficiently inactivate Herpes and Chikungunya viruses

The Amazonian species investigated in this research are commonly utilized for their anti-inflammatory properties and their potential against various diseases. However, there is a lack of scientifically supported information validating their biological activities. In this study, a total of seventeen ethanolic or aqueous extracts derived from eight Amazonian medicinal plants were evaluated for their activity against Herpes Simplex type 1 (HSV-1) and Chikungunya viruses (CHIKV). Cytotoxicity was assessed using the sulforhodamine B method, and the antiviral potential was determined through a plaque number reduction assay. Virucidal tests were conducted according to EN 14476 standards for the most potent extracts. Additionally, the chemical composition of the most active extracts was investigated. Notably, the LMLE10, LMBA11, MEBE13, and VABE17 extracts exhibited significant activity against CHIKV and the non-acyclovir-resistant strain of HSV-1 (KOS) (SI > 9). The MEBE13 extract demonstrated unique inhibition against the acyclovir-resistant strain of HSV-1 (29-R). Virucidal assays indicated a higher level of virucidal activity compared to their antiviral activity. Moreover, the virucidal capacity of the most active extracts was sustained when tested in the presence of protein solutions against HSV-1 (KOS). In the application of EN 14476 against HSV-1 (KOS), the LMBA11 extract achieved a 99.9% inhibition rate, while the VABE17 extract reached a 90% inhibition rate. This study contributes to the understanding of medicinal species native to the Brazilian Amazon, revealing their potential in combating viral infections that have plagued humanity for centuries (HSV-1) or currently lack specific therapeutic interventions (CHIKV).

3D bioprinting tumor models mimic the tumor microenvironment for drug screening

Cancer is a severe threat to human life and health and represents the main cause of death globally. Drug therapy is one of the primary means of treating cancer; however, most anticancer medications do not proceed beyond preclinical testing because the conditions of actual human tumors are not effectively mimicked by traditional tumor models. Hence, bionic in vitro tumor models must be developed to screen for anticancer drugs. Three-dimensional (3D) bioprinting technology can produce structures with built-in spatial and chemical complexity and models with accurately controlled structures, a homogeneous size and morphology, less variation across batches, and a more realistic tumor microenvironment (TME). This technology can also rapidly produce such models for high-throughput anticancer medication testing. This review describes 3D bioprinting methods, the use of bioinks in tumor models, and in vitro tumor model design strategies for building complex tumor microenvironment features using biological 3D printing technology. Moreover, the application of 3D bioprinting in vitro tumor models in drug screening is also discussed.

A Human Ovarian Tumor & Liver Organ-on-Chip for Simultaneous and More Predictive Toxo-Efficacy Assays

In oncology, the poor success rate of clinical trials is becoming increasingly evident due to the weak predictability of preclinical assays, which either do not recapitulate the complexity of human tissues (i.e., in vitro tests) or reveal species-specific outcomes (i.e., animal testing). Therefore, the development of novel approaches is fundamental for better evaluating novel anti-cancer treatments. Here, a multicompartmental organ-on-chip (OOC) platform was adopted to fluidically connect 3D ovarian cancer tissues to hepatic cellular models and resemble the systemic cisplatin administration for contemporarily investigating drug efficacy and hepatotoxic effects in a physiological context. Computational fluid dynamics was performed to impose capillary-like blood flows and predict cisplatin diffusion. After a cisplatin concentration screening using 2D/3D tissue models, cytotoxicity assays were conducted in the multicompartmental OOC and compared with static co-cultures and dynamic single-organ models. A linear decay of SKOV-3 ovarian cancer and HepG2 liver cell viability was observed with increasing cisplatin concentration. Furthermore, 3D ovarian cancer models showed higher drug resistance than the 2D model in static conditions. Most importantly, when compared to clinical therapy, the experimental approach combining 3D culture, fluid-dynamic conditions, and multi-organ connection displayed the most predictive toxicity and efficacy results, demonstrating that OOC-based approaches are reliable 3Rs alternatives in preclinic.

Zebrafish as a Mainstream Model for In Vivo Systems Pharmacology and Toxicology

Pharmacology and toxicology are part of a much broader effort to understand the relationship between chemistry and biology. While biomedicine has necessarily focused on specific cases, typically of direct human relevance, there are real advantages in pursuing more systematic approaches to characterizing how health and disease are influenced by small molecules and other interventions. In this context, the zebrafish is now established as the representative screenable vertebrate and, through ongoing advances in the available scale of genome editing and automated phenotyping, is beginning to address systems-level solutions to some biomedical problems. The addition of broader efforts to integrate information content across preclinical model organisms and the incorporation of rigorous analytics, including closed-loop deep learning, will facilitate efforts to create systems pharmacology and toxicology with the ability to continuously optimize chemical biological interactions around societal needs. In this review, we outline progress toward this goal.

Safety evaluations of a synthetic antimicrobial peptide administered intravenously in rats and dogs

The antimicrobial peptide SET-M33 is under study for the development of a new antibiotic against major Gram-negative pathogens. Here we report the toxicological evaluation of SET-M33 administered intravenously to rats and dogs. Dose range finding experiments determined the doses to use in toxicokinetic evaluation, clinical biochemistry analysis, necroscopy and in neurological and respiratory measurements. Clinical laboratory investigations in dogs and rats showed a dose-related increase in creatinine and urea levels, indicating that the kidneys are the target organ. This was also confirmed by necroscopy studies of animal tissues, where signs of degeneration and regeneration were found in kidney when SET-M33 was administered at the highest doses in the two animal species. Neurological toxicity measurements by the Irwin method and respiratory function evaluation in rats did not reveal any toxic effect even at the highest dose. Finally, repeated administration of SET-M33 by short infusion in dogs revealed a no-observed-adverse-effect-level of 0.5 mg/kg/day.

Predictive Model for Drug-Induced Liver Injury Using Deep Neural Networks Based on Substructure Space

Drug-induced liver injury (DILI) is a major concern for drug developers, regulators, and clinicians. However, there is no adequate model system to assess drug-associated DILI risk in humans. In the big data era, computational models are expected to play a revolutionary role in this field. This study aimed to develop a deep neural network (DNN)-based model using extended connectivity fingerprints of diameter 4 (ECFP4) to predict DILI risk. Each data set for the predictive model was retrieved and curated from DILIrank, LiverTox, and other literature. The best model was constructed through ten iterations of stratified 10-fold cross-validation, and the applicability domain was defined based on integer ECFP4 bits of the training set which represented substructures. For the robustness test, we employed the concept of the endurance level. The best model showed an accuracy of 0.731, a sensitivity of 0.714, and a specificity of 0.750 on the validation data set in the complete applicability domain. The model was further evaluated with four external data sets and attained an accuracy of 0.867 on 15 drugs with DILI cases reported since 2019. Overall, the results suggested that the ECFP4-based DNN model represents a new tool to identify DILI risk for the evaluation of drug safety.