In silico models in drug development: where we are

The importance of preclinical models in cholangiocarcinoma

Cholangiocarcinoma (CCA) is an adenocarcinoma of the hepatobiliary system with a grim prognosis. Incidence is rising globally and surgery is currently the only curative treatment, but is only available for patients who are fit and diagnosed in an early-stage of disease progression. Great importance has been placed on developing preclinical models to help further our understanding of CCA and potential treatments to improve therapeutic outcomes. Preclinical models of varying complexity and cost have been established, ranging from more simplistic in vitro 2D CCA cell lines in culture, to more complex in vivo genetically engineered mouse models. Currently there is no single model that faithfully recaptures the complexities of human CCA and the in vivo tumour microenvironment. Instead a multi-model approach should be used when designing preclinical trials to study CCA and potential therapies.

Application of Model-Informed Drug Development in Dose Selection and Optimization for siRNA Therapies

The application of model-informed drug development (MIDD) has revolutionized drug development and regulatory decision making, transforming the process into one that is more efficient, effective, and patient centered. A critical application of MIDD is to facilitate dose selection and optimization, which play a pivotal role in improving efficacy, safety, and tolerability profiles of a candidate drug. With the surge of interest in small interfering RNA (siRNA) drugs as a promising class of therapeutics, their applications in various disease areas have been extensively studied preclinically. However, dosing selection and optimization experience for siRNA in humans is limited. Unique challenges exist for the dose evaluation of siRNA due to the temporal discordance between pharmacokinetic and pharmacodynamic profiles, as well as limited available clinical experience and considerable interindividual variability. This review highlights the pivotal role of MIDD in facilitating dose selection and optimization for siRNA therapeutics. Based on past experiences with approved siRNA products, MIDD has demonstrated its ability to aid in dose selection for clinical trials and enabling optimal dosing for the general patient population. In addition, MIDD presents an opportunity for dose individualization based on patient characteristics, enhancing the precision and effectiveness of siRNA therapeutics. In conclusion, the integration of MIDD offers substantial advantages in navigating the complex challenges of dose selection and optimization in siRNA drug development, which in turn accelerates the development process, supports regulatory decision making, and ultimately improves the clinical outcomes of siRNA-based therapies, fostering advancements in precision medicine across a diverse range of diseases.

Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models

In silico methods can be used for an early assessment of arrhythmogenic properties of drug candidates. However, their use for decision-making is conditioned by the possibility to estimate the predictions' uncertainty. This work describes our efforts to develop uncertainty quantification methods for the predictions produced by multi-level proarrhythmia models. In silico models used in this field usually start with experimental or predicted IC50 values that describe drug-induced ion channel blockade. Using such inputs, an electrophysiological model computes how the ion channel inhibition, exerted by a drug in a certain concentration, translates to an altered shape and duration of the action potential in cardiac cells, which can be represented as arrhythmogenic risk biomarkers such as the APD90. Using this framework, we identify the main sources of aleatory and epistemic uncertainties and propose a method based on probabilistic simulations that replaces single-point estimates predicted using multiple input values, including the IC50s and the electrophysiological parameters, by distributions of values. Two selected variability types associated with these inputs are then propagated through the multi-level model to estimate their impact on the uncertainty levels in the output, expressed by means of intervals. The proposed approach yields single predictions of arrhythmogenic risk biomarkers together with value intervals, providing a more comprehensive and realistic description of drug effects on a human population. The methodology was tested by predicting arrhythmogenic biomarkers on a series of twelve well-characterised marketed drugs, belonging to different arrhythmogenic risk classes.

Experimental Methods for the Biological Evaluation of Nanoparticle-Based Drug Delivery Risks

Many novel medical therapies use nanoparticle-based drug delivery systems, including nanomaterials through drug delivery systems, diagnostics, or physiologically active medicinal products. The approval of nanoparticles with advanced therapeutic and diagnostic potentials for applications in medication and immunization depends strongly on their synthesizing procedure, efficiency of functionalization, and biological safety and biocompatibility. Nanoparticle biodistribution, absorption, bioavailability, passage across biological barriers, and biodistribution are frequently assessed using bespoke and biological models. These methods largely rely on in vitro cell-based evaluations that cannot predict the complexity involved in preclinical and clinical studies. Therefore, assessing the nanoparticle risk has to involve pharmacokinetics, organ toxicity, and drug interactions manifested at multiple cellular levels. At the same time, there is a need for novel approaches to examine nanoparticle safety risks due to increased constraints on animal exploitation and the demand for high-throughput testing. We focus here on biological evaluation methodologies that provide access to nanoparticle interactions with the organism (positive or negative via toxicity). This work aimed to provide a perception regarding the risks associated with the utilization of nanoparticle-based formulations with a particular focus on assays applied to assess the cytotoxicity of nanomaterials.

Ultrasound Guided Surgery as a Refinement Tool in Oncology Research

Refinement is one of the ethical pillars of the use of animals in research. Ultrasonography is currently used in human medicine as a surgical tool for guided biopsies and this idea can be applied to preclinical research thanks to the development of specific instruments. This will eliminate the necessity of a surgical opening for implanting cells in specific organs or taking samples from tissues. The approach for the injection will depend on the target but most of the case is going to be lateral, with the probe in a ventral position and the needle going into from the lateral. This is the situation for the thyroid gland, heart, liver, spleen, kidney, pancreas, uterus, and testicles. Other approaches, such as the dorsal, can be used in the spleen or kidney. The maximum injected volume will depend on the size of the structure. For biopsies, the technical protocol is similar to the injection knowing that in big organs such as the liver, spleen, or kidney we can take several samples moving slightly the needle inside the structure. In all cases, animals must be anesthetized and minimum pain management is required after the intervention.

Topological Distance-Based Electron Interaction Tensor to Apply a Convolutional Neural Network on Drug-like Compounds

Deep learning (DL) models in quantitative structure-activity relationship fed the molecular structure directly to the network without using human-designed descriptors by representing molecule as a graph or string (e.g., SMILES code). However, these two representations were oversimplification of real molecules to reflect chemical properties of molecular structures. Given that the choice of molecular representation determines the architecture of the DL model to apply, a novel way of molecular representation can open a way to apply diverse DL networks developed and used in other fields. A topological distance-based electron interaction (TDEi) tensor has been developed in this study inspired by the quantum mechanical model of the molecule, which defines a molecule with electrons and protons. In the TDEi tensor, the atomic orbital (AO) of each atom is represented by an electron configuration (EC) vector, which is a bit string based on the presence and absence of electrons in each AO according to spin indicated by positive and negative signs. Interactions between EC vectors were calculated based on the topological distance between atoms in a molecule. As a molecular structure was translated into 3D array, CNN models (modified VGGNet) were applied using a TDEi tensor to predict four physicochemical properties of drug-like compound datasets: MP (275,131), Lipop (4193), Esol (1127), and Freesolv (639). Models achieved good prediction accuracy. PCA showed that a stronger correlation was observed between the extracted features and the target endpoint as features were extracted from the deeper layer.

How Physical Factors Coordinate Virus Infection: A Perspective From Mechanobiology

Pandemics caused by viruses have threatened lives of thousands of people. Understanding the complicated process of viral infection provides significantly directive implication to epidemic prevention and control. Viral infection is a complex and diverse process, and substantial studies have been complemented in exploring the biochemical and molecular interactions between viruses and hosts. However, the physical microenvironment where infections implement is often less considered, and the role of mechanobiology in viral infection remains elusive. Mechanobiology focuses on sensation, transduction, and response to intracellular and extracellular physical factors by tissues, cells, and extracellular matrix. The intracellular cytoskeleton and mechanosensors have been proven to be extensively involved in the virus life cycle. Furthermore, innovative methods based on micro- and nanofabrication techniques are being utilized to control and modulate the physical and chemical cell microenvironment, and to explore how extracellular factors including stiffness, forces, and topography regulate viral infection. Our current review covers how physical factors in the microenvironment coordinate viral infection. Moreover, we will discuss how this knowledge can be harnessed in future research on cross-fields of mechanobiology and virology.

Bridging the academia-to-industry gap: organ-on-a-chip platforms for safety and toxicology assessment

Some organ-on-a-chip (OoC) systems for drug evaluation show better predictive capabilities than planar, static cell cultures and animal models. One of the ongoing initiatives led by OoC developers is to bridge the academia-to-industry gap in the hope of gaining wider adoption by end-users - academic biological researchers and industry. We discuss several recommendations that can help to drive the adoption of OoC systems by the market. We first review some key challenges faced by OoC developers before highlighting current advances in OoC platforms. We then offer recommendations for OoC developers to promote the uptake of OoC systems by the industry.

Airway-On-A-Chip: Designs and Applications for Lung Repair and Disease

The lungs are affected by illnesses including asthma, chronic obstructive pulmonary disease, and infections such as influenza and SARS-CoV-2. Physiologically relevant models for respiratory conditions will be essential for new drug development. The composition and structure of the lung extracellular matrix (ECM) plays a major role in the function of the lung tissue and cells. Lung-on-chip models have been developed to address some of the limitations of current two-dimensional in vitro models. In this review, we describe various ECM substitutes utilized for modeling the respiratory system. We explore the application of lung-on-chip models to the study of cigarette smoke and electronic cigarette vapor. We discuss the challenges and opportunities related to model characterization with an emphasis on in situ characterization methods, both established and emerging. We discuss how further advancements in the field, through the incorporation of interstitial cells and ECM, have the potential to provide an effective tool for interrogating lung biology and disease, especially the mechanisms that involve the interstitial elements.

Current Strategies in Assessment of Nanotoxicity: Alternatives to In Vivo Animal Testing

Millions of experimental animals are widely used in the assessment of toxicological or biological effects of manufactured nanomaterials in medical technology. However, the animal consciousness has increased and become an issue for debate in recent years. Currently, the principle of the 3Rs (i.e., reduction, refinement, and replacement) is applied to ensure the more ethical application of humane animal research. In order to avoid unethical procedures, the strategy of alternatives to animal testing has been employed to overcome the drawbacks of animal experiments. This article provides current alternative strategies to replace or reduce the use of experimental animals in the assessment of nanotoxicity. The currently available alternative methods include in vitro and in silico approaches, which can be used as cost-effective approaches to meet the principle of the 3Rs. These methods are regarded as non-animal approaches and have been implemented in many countries for scientific purposes. The in vitro experiments related to nanotoxicity assays involve cell culture testing and tissue engineering, while the in silico methods refer to prediction using molecular docking, molecular dynamics simulations, and quantitative structure-activity relationship (QSAR) modeling. The commonly used novel cell-based methods and computational approaches have the potential to help minimize the use of experimental animals for nanomaterial toxicity assessments.

Engineering Advanced In Vitro Models of Systemic Sclerosis for Drug Discovery and Development

Systemic sclerosis (SSc) is a complex multisystem disease with the highest case-specific mortality among all autoimmune rheumatic diseases, yet without any available curative therapy. Therefore, the development of novel therapeutic antifibrotic strategies that effectively decrease skin and organ fibrosis is needed. Existing animal models are cost-intensive, laborious and do not recapitulate the full spectrum of the disease and thus commonly fail to predict human efficacy. Advanced in vitro models, which closely mimic critical aspects of the pathology, have emerged as valuable platforms to investigate novel pharmaceutical therapies for the treatment of SSc. This review focuses on recent advancements in the development of SSc in vitro models, sheds light onto biological (e.g., growth factors, cytokines, coculture systems), biochemical (e.g., hypoxia, reactive oxygen species) and biophysical (e.g., stiffness, topography, dimensionality) cues that have been utilized for the in vitro recapitulation of the SSc microenvironment, and highlights future perspectives for effective drug discovery and validation.

Delivery Mechanism of the Pharmaceutical Complex of Genistein-Adenine Based on Spectroscopic and Molecular Modelling at Atomic Scale

Genistein (GS) exhibits various biological activities, but its clinical application is limited because of the low bioavailability. In this study, a GS-adenine pharmaceutical complex was prepared through solvent evaporation to improve the bioavailability of GS, and a molecular model of a two-component supramolecular pharmacological transport mechanism was established. The structure of GS-adenine was characterized, in addition, interaction patterns between GS and adenine were investigated using density functional theory. The results showed that the solubility of GS-adenine was five times higher than that of GS, and the cumulative release rate of GS-adenine was 86 %. The results of fluorescence spectroscopy and molecular dynamic simulations showed that GS-adenine bound to the Sudlow's site I of HSA mainly through hydrophobic interactions. This study provides a useful reference for synthesizing pharmaceutical complexes to improve solubility and for exploring the mechanism of multiple pharmaceutical components in vivo.

Harnessing the power of novel animal-free test methods for the development of COVID-19 drugs and vaccines

The COVID-19-inducing virus, SARS-CoV2, is likely to remain a threat to human health unless efficient drugs or vaccines become available. Given the extent of the current pandemic (people in over one hundred countries infected) and its disastrous effect on world economy (associated with limitations of human rights), speedy drug discovery is critical. In this situation, past investments into the development of new (animal-free) approach methods (NAM) for drug safety, efficacy, and quality evaluation can be leveraged. For this, we provide an overview of repurposing ideas to shortcut drug development times. Animal-based testing would be too lengthy, and it largely fails, when a pathogen is species-specific or if the desired drug is based on specific features of human biology. Fortunately, industry has already largely shifted to NAM, and some public funding programs have advanced the development of animal-free technologies. For instance, NAM can predict genotoxicity (a major aspect of carcinogenicity) within days, human antibodies targeting virus epitopes can be generated in molecular biology laboratories within weeks, and various human cell-based organoids are available to test virus infectivity and the biological processes controlling them. The European Medicines Agency (EMA) has formed an expert group to pave the way for the use of such approaches for accelerated drug development. This situation illustrates the importance of diversification in drug discovery strategies and clearly shows the shortcomings of an approach that invests 95% of resources into a single technology (animal experimentation) in the face of challenges that require alternative approaches.

Upcoming Revolutionary Paths in Preclinical Modeling of Pancreatic Adenocarcinoma

To date, PDAC remains the cancer having the worst prognosis with mortality rates constantly on the rise. Efficient cures are still absent, despite all attempts to understand the aggressive physiopathology underlying this disease. A major stumbling block is the outdated preclinical modeling strategies applied in assessing effectiveness of novel anticancer therapeutics. Current in vitro preclinical models have a low fidelity to mimic the exact architectural and functional complexity of PDAC tumor found in human set, due to the lack of major components such as immune system and tumor microenvironment with its associated chemical and mechanical signals. The existing PDAC preclinical platforms are still far from being reliable and trustworthy to guarantee the success of a drug in clinical trials. Therefore, there is an urgent demand to innovate novel in vitro preclinical models that mirrors with precision tumor-microenvironment interface, pressure of immune system, and molecular and morphological aspects of the PDAC normally experienced within the living organ. This review outlines the traditional preclinical models of PDAC namely 2D cell lines, genetically engineered mice, and xenografts, and describing the present famous approach of 3D organoids. We offer a detailed narration of the pros and cons of each model system. Finally, we suggest the incorporation of two off-center newly born techniques named 3D bio-printing and organs-on-chip and discuss the potentials of swine models and in silico tools, as powerful new tools able to transform PDAC preclinical modeling to a whole new level and open new gates in personalized medicine.

Perspectives of Pharmacology over the Past 100 Years

It is fitting that the 100th anniversary of the Handbook of Experimental Pharmacology celebrates not only its founding but also the founding of experimental pharmacology as both had their beginnings in Germany. Founded in 1919 by Arthur Heffter (1859-1925) as the "Handbuch der Experimentellen Pharmakologie" and renamed to its current title in 1937, the Handbook has continued to capture the emergence and developments of experimental pharmacology since the initial systematic work of Rudolf Buchheim and his student Oswald Schmiedeberg. Heffter, the first Chairman of the German Society of Pharmacology, was also responsible for isolating mescaline as the active psychedelic component from the peyote cactus, thereby initiating a series of studies along with an Institute that, much like the Handbook and the discipline of pharmacology, continues to discover and disseminate new findings to this day. These early endeavors to establish pharmacology as a viable and valuable contributor to the medical sciences met with considerable resistance and challenges. However, the persistence and dedication of these early pharmacologists placed pharmacology on a firm foundation from which to spread this discipline globally, leading ultimately to our current understanding of the principles of drug action and with an impact likely unanticipated by these founding scientists. Summarizing the beginnings of these efforts and their early spread to other countries provides an appropriate context in which to document the many contributions pharmacological research has made over the past 100 years and provide an opportunity to anticipate expectations around its future developments.

Predicting and Understanding the Human Microbiome's Impact on Pharmacology

Our bodies each possess a unique and dynamic collection of microbes and viruses, collectively the 'microbiome', with distinct metabolic capacities from our human cells. Unforeseen modification of drugs by the microbiome can drastically alter their clinical effectiveness, with the most dramatic cases leading to fatal drug interactions. Pharmaceuticals can be activated, deactivated, toxified, or release metabolites that alter the 'canonical' pharmacokinetics of the drug. Thus, predicting and characterizing microbe-drug interactions is necessary to develop and implement personalized drug administration protocols and, more broadly, to improve drug safety and efficacy. In this review, we focus on microbiome-driven alterations to drug pharmacokinetics and provide a research framework for pharmacologists interested in characterizing microbiome interactions with any drug of interest.

Unlocking the potential of in silico chemical safety assessment - A report on a cross-sector symposium on current opportunities and future challenges

In silico chemical safety assessment can support the evaluation of hazard and risk following potential exposure to a substance. A symposium identified a number of opportunities and challenges to implement in silico methods, such as quantitative structure-activity relationships (QSARs) and read-across, to assess the potential harm of a substance in a variety of exposure scenarios, e.g. pharmaceuticals, personal care products, and industrial chemicals. To initiate the process of in silico safety assessment, clear and unambiguous problem formulation is required to provide the context for these methods. These approaches must be built on data of defined quality, while acknowledging the possibility of novel data resources tapping into on-going progress with data sharing. Models need to be developed that cover appropriate toxicity and kinetic endpoints, and that are documented appropriately with defined uncertainties. The application and implementation of in silico models in chemical safety requires a flexible technological framework that enables the integration of multiple strands of data and evidence. The findings of the symposium allowed for the identification of priorities to progress in silico chemical safety assessment towards the animal-free assessment of chemicals.