INTRODUCTION:Three-dimensional cell culture systems, such as spheroids and organoids, have emerged as tools for modeling biological processes beyond conventional two-dimensional cultures. Spheroids, formed by self-aggregation of cell lines or primary cells, are simple multicellular structures ideal for drug screening due to their reproducibility and compatibility with high-throughput platforms. Organoids, typically derived from stem cells or patient tissues, develop structures that mimic tissue architecture and genetic features, including tumor heterogeneity. AREAS COVERED:This review examines the fundamental differences between spheroid and organoid models, focusing on their cellular origin, structural features, methods of generation, and applications in tumor research. The authors discuss the advantages and limitations of each system and highlight how experimental goals, scalability requirements, and biological complexity influence model selection. This review is based on structured literature searches of PubMed/MEDLINE, Web of Science, and Google Scholar for articles published between January 1988 and July 2026. EXPERT OPINION:Tumor spheroid models offer practical and scalable solutions to screening and mechanistic studies, while organoids offer biological complexity and better representation of patient-derived disease characteristics. These models improve the physiological relevance of in-vitro studies, help bridge the gap between simplified cell culture systems and in-vivo testing, and reduce dependence on animal model testing.
INTRODUCTION:Interstitial cystitis/bladder pain syndrome (IC/BPS) is a chronic condition characterized by bladder pain and lower urinary tract symptoms. Despite extensive investigation and numerous therapeutic candidates, few disease-modifying treatments have translated into clinical practice. Evidence indicates that IC/BPS comprises biologically distinct phenotypes, including inflammatory bladder-centric and systemic pain-associated phenotypes, creating challenges for experimental modeling and drug development. AREAS COVERED:Using literature from a structured search of PubMed, Scopus, and Web of Science, this review examines current IC/BPS model systems from a drug discovery perspective, including preclinical, human translational, and computational approaches. The authors evaluate the biological domains captured by existing models, their strengths and limitations, and their influence on therapeutic development. Attention is given to four translational challenges: inadequate representation of disease heterogeneity, reliance on acute injury paradigms for a chronic pain condition, bladder-centric modeling of a multisystem disorder, and limited integration of computational phenotyping. Finally, the authors propose a model-informed framework linking experimental systems with therapeutic mechanisms, patient phenotypes, and clinically relevant endpoints. EXPERT OPINION:Progress in IC/BPS drug discovery will depend on mechanism-driven use of complementary models aligned with defined patient subgroups. Integrating patient phenotyping, biomarkers, computational methods, and back-translation from clinical trial outcomes may improve translational predictability and therapeutic success.
INTRODUCTION:Acute myeloid leukemia (AML) is an aggressive hematological malignancy associated with poor prognosis, high rates of chemoresistance and disease relapse. High attrition rates in the drug development pipeline highlight the need for improved modeling strategies that provide more reliable translation from preclinical to clinical outcomes. Bioengineered human cell-based bone marrow models are emerging as promising platforms capable of recapitulating key aspects of disease biology and therapeutic response more faithfully than conventional models. AREAS COVERED:This review discusses advances in bioengineered bone marrow models of AML, including systems capable of reproducing chemoresistance, off-target toxicities, leukemic niche remodeling, and microenvironment-mediated disease mechanisms that are often overlooked by conventional two-dimensional cultures and current animal models. The physical, cellular, and biochemical properties to be considered in next-generation models are also examined. Literature was identified through searches of PubMed, Science Direct, and Google Scholar. EXPERT OPINION:Bioengineered AML bone marrow models have the potential to improve drug development by identifying ineffective compounds earlier and enabling investigation of patient-specific disease heterogeneity. Future progress will depend not only on increasing biological complexity through technologies such as organoids and organ-on-chip systems, but also on rigorous validation against patient data, standardization, scalability, and regulatory acceptance.
INTRODUCTION:Epigenetic drug discovery remains a promising drug discovery strategy that has long been driven by advances in computational approaches. The subfield of epi-informatics, established more than a decade ago, continues to evolve rapidly as emerging machine learning methodologies reshape and expand its applications. AREAS COVERED:The authors provide an updated overview of bioinformatics, chemoinformatics, and machine learning methodologies used to identify, design, and optimize compounds, primarily small-molecules, that modulate epigenetic processes with therapeutic potential. The discussion is based on a comprehensive literature analysis of peer-reviewed literature, encompassing 7,185 unique research articles published between 2000 up to 2025. The article also examines the epigenetic drug discovery landscape by analyzing the most extensively investigated epigenetic targets and emerging research trends. EXPERT OPINION:Epi-informatics has evolved into a distinct interdisciplinary field integrating bioinformatics, chemoinformatics, and artificial intelligence to advance epigenetic drug discovery. Although rapid progress in multi-omics integration, molecular modeling, and generative artificial intelligence is accelerating the identification of drug candidates, future success will depend on high-quality, standardized data, interpretable machine learning models, and rigorous experimental validation that ensure reproducibility. Addressing these challenges will further advance epi-informatics in oncology research and an expanding range of complex diseases.
INTRODUCTION:Depression is a heterogeneous and multifactorial condition with substantial unmet therapeutic need. Despite significant research, current pharmacotherapies remain limited in efficacy and onset, reflecting an incomplete understanding of underlying pathophysiology. Animal models have therefore been central to dissecting biological mechanisms and enabling antidepressant drug discovery. AREA COVERED:This review provides a synthesis of animal models of depression, including stress paradigms, developmental and genetic models, neuroendocrine manipulations, immune-based, metabolic and hormone-related models. PubMed was searched from database inception to March 2026 using depression-, species-, and model-specific terms. Each paradigm is evaluated in terms of face, construct, and predictive validity, with emphasis on relevance to drug discovery. Converging evidence across models highlights shared pathophysiological domains, including HPA axis dysregulation, neuroinflammation, monoaminergic and glutamatergic imbalance, and impaired neuroplasticity. EXPERT OPINION:No single model captures the complexity of depression; thus, reliance on isolated paradigms may contribute to translational failure. Future progress will depend on multimodal and endophenotype-driven approaches that align biological mechanisms with defined subtypes (e.g. inflammation-associated, hormone-sensitive, or stress-related depression). Integration of animal models with human-relevant data and sex-informed experimental design will advance precision psychiatry, improving predictive validity of preclinical antidepressant discovery.
INTRODUCTION:RNA viruses impose a disproportionate burden of emerging and reemerging infectious diseases in Latin America, yet antiviral discovery in the region remains fragmented, target-limited, and frequently disconnected from translational pathways. This perspective discusses the Red Mexicana de Desarrollo de Antivirales (RMDA) as a network-driven model to organize antiviral discovery against clinically relevant RNA viruses. AREAS COVERED:The authors describe the conceptual architecture of RMDA, a multi-institutional Mexican initiative that integrates computational prioritization, cell-based antiviral evaluation under BSL-2/BSL-3 conditions, mechanistic virology, assessment of pharmacological synergy, in vivo validation, and clinical linkage through healthcare systems such as the Instituto Mexicano del Seguro Social. The framework is supported by prior work from participating groups on host-directed and virus-directed candidates, including metabolic modulators, lipid-lowering agents, nuclear transport inhibitors, natural products, polyphenols, melatonin, and rational drug combinations evaluated against dengue virus, Zika virus, SARS-CoV-2, respiratory syncytial virus, and related RNA viruses. EXPERT OPINION:RMDA provides a scalable and regionally adaptable model for antiviral drug discovery in low- and middle-income settings. By aligning distributed expertise, standardized workflows, and translational decision points, this framework may accelerate candidate prioritization, reduce duplication, and strengthen preparedness against RNA virus threats in Mexico and Latin America.
INTRODUCTION:Cardiovascular diseases remain a major cause of morbidity and mortality, and many disease-relevant RNA mechanisms remain difficult to address with conventional therapeutic modalities. Antisense oligonucleotides (ASOs) provide a sequence-defined RNA-targeting modality to modulate transcript abundance, splicing, and regulatory RNA function. In cardiovascular drug discovery, however, target complementarity is only the starting point. Translational success requires early alignment between target biology, tissue exposure, and therapeutic index. AREAS COVERED:Based on PubMed and Web of Science searches through June 2026, this review discusses the principles that shape cardiovascular ASO candidate development, with emphasis on mechanism selection, chemical design, and exposure feasibility. Selected examples from lipoprotein-related targets and transthyretin amyloidosis are used to illustrate why target compartment and pharmacodynamic evidence are central to translational decision-making. EXPERT OPINION:The near-term impact of cardiovascular ASO therapeutics is likely to be strongest for targets in accessible compartments, particularly liver-derived mediators with clear links to cardiovascular pathology. Applications requiring direct engagement of cardiovascular tissues, including vascular and myocardial targets, will require evidence that target engagement can be achieved in the relevant cell populations at tolerable exposure levels. Future development should therefore integrate sequence optimization with exposure-informed target qualification and therapeutic-index engineering throughout ASO candidate selection.
INTRODUCTION:This review examines how Foundation Models can address critical limitations of data scarcity in drug discovery, particularly for neglected diseases where traditional approaches are ineffective. It highlights the need for new methodologies that integrate heterogeneous data sources to enable more equitable and efficient therapeutic development. AREAS COVERED:This review synthesizes recent advances in Foundation Models and related machine learning approaches for low-data drug discovery, with a focus on applications in neglected diseases. The authors searched PubMed, Scopus, and Web of Science for available literature using terms related to foundation models, machine learning, deep learning, AI with neglected diseases, drug discovery, low-data settings, transfer learning, and related methodological and disease-specific terms. Reference lists of included reviews were additionally screened for relevant primary literature. EXPERT OPINION:Foundation Models are poised to play a central role in drug discovery. Nevertheless, their effectiveness for low-data and neglected diseases will depend on strong collaboration, responsible and ethical use, and continued technical innovation.
INTRODUCTION:Iron- and iron-sulfur cluster (Fe-S)-containing proteins are essential for diverse biological processes, including electron transfer, genome maintenance, metabolism, cellular signaling, and host-pathogen interactions. Despite their broad biological importance and growing links to human disease, Fe-S cluster-dependent proteins remain underexplored as therapeutic targets, largely because it is difficult to define their metal-dependent chemistry using conventional biochemical, spectroscopic, and structural approaches. AREAS COVERED:This review examines how Mössbauer spectroscopy can be integrated into workflows for metalloprotein characterization, target validation, and drug discovery. Using representative Fe-S cluster-containing proteins, the practical considerations for implementing Mössbauer spectroscopy are outlined, including 57Fe-enriched expression, sample preparation, and spectroscopic analysis. Two case studies of experimentally challenging viral Fe-S cluster proteins are then highlighted, the Hepatitis B virus X protein and the Porcine Reproductive and Respiratory Syndrome Virus Nsp1α protease, which demonstrate how direct characterization of metal cofactors can reveal previously unrecognized therapeutic avenues. Relevant literature published through March 2026 was identified using PubMed and Google Scholar with keywords related to Mössbauer spectroscopy, iron-sulfur proteins, viral metalloproteins, and drug discovery. EXPERT OPINION:As drug discovery increasingly seeks to exploit metal-dependent biology, Mössbauer spectroscopy will play an important role in identifying cryptic metalloproteins, defining their native states, and uncovering Fe- and Fe-S cluster-dependent targets. Mössbauer spectroscopy can also be complementary, and integrated with structural and AI-driven approaches to answer emerging challenges in medicinal chemistry.
INTRODUCTION:Marburg virus disease (MVD) occurs in sporadic and unpredictable outbreaks, making conventional human efficacy studies of vaccines and therapeutics impractical. This review summarizes animal models of MVD and their utility for pathogenesis research and evaluation of vaccines and therapeutics. AREAS COVERED:Herein, the authors review rodent, ferret, and nonhuman primate models of MVD infection, focusing on how each reflects human disease and supports different stages of countermeasure development. Furthermore, the authors discuss their virological and pathological features and major strengths and limitations, with emphasis on the influences of the host background, viral adaptation, inoculation route, and study design on translational relevance. A literature search was conducted using PubMed, ScienceDirect, Web of Science, and Google Scholar. EXPERT OPINION:Since no single model fully captures human MVD, model choice should be guided by specific scientific or translational questions rather than human relevance alone. Stepwise evaluation is required, with mice supporting mechanistic and early screening, other rodents and ferrets enabling candidate refinement, and nonhuman primates providing definitive efficacy evaluation. Although some models with host-adapted viruses impose limitations, they remain useful when their biological consequences are defined. Animal models are indispensable for MVD drug development, and further harmonization of strains, endpoints, and reporting standards will improve their predictive value.
INTRODUCTION:Drug discovery remains constrained by high attrition rates and the fragmented evaluation of exposure, efficacy, and safety. Mechanistic models offer a biologically grounded framework for connecting these determinants across multiple levels of biological organization. This may help improve translational decision-making by supporting earlier and more integrated assessment of candidate progression. AREAS COVERED:This narrative review examines the conceptual basis and current role of next-generation mechanistic models in drug discovery, with emphasis on physiologically based pharmacokinetic models, virtual cell-based assays, quantitative systems pharmacology, artificial intelligence (AI)-augmented mechanistic models, and emerging virtual-cell frameworks. It highlights how these approaches may connect efficacy and safety across biological scales, support in vitro-to-in vivo extrapolation, incorporate in silico predictions, and improve candidate prioritization. The literature was surveyed through PubMed searches conducted up to 25 May 2026. EXPERT OPINION:Next-generation mechanistic models are unlikely to transform drug discovery simply by increasing biological detail or computational sophistication. Progress in this direction will depend on standardized data streams, robust validation, explicit model calibration, reproducibility, tighter integration between models, and careful alignment between model design and context of use. Under these conditions, mechanistic frameworks may become important components of a more predictive and less attrition-prone drug discovery pipeline.
Introduction Ibogaine is a naturally occurring indole alkaloid with suggested therapeutic potential across substance use disorders, trauma-related conditions, mood disorders, and suicidality. However, its clinical translation has been hindered by safety concerns, regulatory barriers, and uncertainty regarding its complex pharmacology. Recent interest has surged in developing ibogaine analogs and derivatives that retain therapeutic efficacy while minimizing safety risks. Ibogaine pharmacology is complex with many affected targets, which complicates these efforts.Areas covered Herein, the authors propose a conceptual framework that distinguishes between two primary strategies: (1) development of ibogaine-like compounds that preserve broad, polypharmacological effects while mitigating key safety liabilities, and (2) creation of more selective, purpose-built 'bespoke' analogs designed to optimally target specific neurobiological pathways and clinical indications-such as opioid use disorder (OUD), traumatic brain injury, or post-traumatic stress disorder (PTSD). Furthermore, the authors critically evaluate the current evidence supporting each approach, and discuss the translational trade-offs related to safety, efficacy, comorbidity, and scalability. The authors also highlight the importance of individual variability, including pharmacogenetics in treatment response.Expert opinion It is important, and particularly within policy-driven research initiatives, that this evolving field must resist oversimplified narratives that frame derivatives as uniformly superior or interchangeable. Greater conceptual clarity and mechanistic humility are also essential as ibogaine-based therapies move toward regulated medical use within Westernized healthcare models.
Introduction Mitochondrial safety assessment is used in drug discovery, supported by bioenergetic profiling, mechanistic assays, human-relevant cellular systems, and multidimensional data. These advances have improved detection of mitochondrial perturbation but have not solved the harder problem: how such signals should be interpreted and translated into discovery decisions.Areas covered This perspective proposes a qualitative decision-centered framework for interpreting mitochondrial findings. This framework is anchored in reserve-demand biology, which explains why mitochondrial perturbations become consequential when drug-induced reductions in bioenergetic capacity intersect with tissue-specific demand, exposure, duration, and stress context. Furthermore, the authors describe a qualitative Translational Risk Profile organized around mechanistic severity, exposure relevance, temporal progression, and translational concordance. This profile is paired with a Decision Taxonomy: Stop, Optimize, Monitor, or Acceptable Risk. Examples illustrate how mitochondrial evidence patterns can support different discovery actions.Expert opinion The major limitation in mitochondrial safety assessment is no longer signal detection, but decision-oriented interpretation. Future progress will depend on integrating mechanism, exposure, duration, biomarkers, human-relevant models and quantitative or computational evidence into explicit decision frameworks. Mitochondrial findings should not be treated as binary hazards. They should be interpreted as context-dependent evidence that can guide chemistry, candidate selection, monitoring strategy, and translational risk management.
INTRODUCTION:Alzheimer's Disease (AD) affects more than 57 million people, yet drug development faces high failure rates due to the limited translational validity of conventional models. This review analyzes how 3D (three-dimensional) systems provide a technical validation platform that more accurately replicates the human brain microenvironment. AREAS COVERED:This review analyzes the evolution of 3D culture systems, including spheroids, organoids, hydrogels, and microfluidics. A comprehensive literature search was conducted in PubMed, Scopus, Web of Science, and ScienceDirect, covering publications from January 2014 to March 2026. The analysis synthesizes evidence on the capacity of these models to reproduce beta-amyloid (β-amyloid) aggregation and tau hyperphosphorylation, while exploring the integration of artificial intelligence to optimize compound screening. EXPERT OPINION:3D models serve as high-fidelity biological filters that complement current preclinical methodologies, improving early identification of toxicities and therapeutic efficacy. Their strategic integration and technical standardization are essential steps to refine predictive accuracy and optimize the transition to clinical trials.
INTRODUCTION:ALS drug discovery has long depended on model systems that incompletely capture human disease heterogeneity, aging, and TDP-43 proteinopathy. Patient-derived platforms have therefore emerged as increasingly important human-relevant complements to animal and molecular models. AREAS COVERED:This Critical Perspective examines when patient-derived ALS models genuinely change therapeutic decision-making rather than merely add mechanistic insight. The authors then propose a heuristic framework based on disease-relevant phenotype recapitulation, capture of patient-to-patient heterogeneity, and generation of findings that influence therapeutic prioritization or clinical translation. Furthermore, the authors evaluate iPSC-derived motor neurons, directly reprogrammed neurons, glial co-cultures, organoids, neural networks, and organ-chip systems against these conditions, while also addressing aging fidelity, reproducibility, upper motor neuron modeling, and regulatory implementation. EXPERT OPINION:Patient-derived models are not yet standalone decision-grade tools for ALS drug development. Their present value lies in functioning as a human-biology filter for target discovery, reverse translation, biomarker development, and patient stratification when used within rigorous, standardized, and clinically linked workflows. The strongest current evidence supports proof-of-principle rather than generalized predictive validity.
