A lot of capital is pouring into artificial intelligence (AI) that designs new molecules, while a parallel supply of de-risked, clinical-stage drugs is overlooked. Strategic and business decisions, in the absence of scientific failure, account for a substantial and rising share of late-stage terminations. This review distinguishes strategic recovery, advancing clinical-stage assets shelved without scientific failure, from repurposing and rescue. Meanwhile, a historic patent cliff is pressing companies to refill pipelines faster than novel discovery allows. Recovering these drugs is commercially proven, and a large and fast-growing reservoir exists in China, whose registries are poorly indexed against Western ontologies. This review argues that agentic AI applied to multilingual trial corpora makes recovery possible and asks which open, clinically governed infrastructure could bring these medicines to patients.
Isocitrate dehydrogenase (IDH) enzymes convert isocitrate to α-ketoglutarate. When IDH1 or IDH2 is mutated, the enzyme gains a new function, and the oncometabolite D-2-hydroxyglutarate (D-2-HG) accumulates. Its epigenetic and metabolic effects depend on the tumor context. This review classifies mutant IDH inhibitors by chemical scaffold and relates their binding in the allosteric pocket to structure-activity trends, isoform selectivity, brain penetration, and clinical outcome. Mutant IDH1, mutant IDH2, pan-IDH, and covalent inhibitors are compared, with lessons from successful and failed clinical candidates. Resistance is treated separately: secondary mutations, isoform switching, metabolic adaptation, rational combinations, PROTAC degraders, and biomarkers. Since reduced 2-HG indicates target engagement rather than clinical benefit, design priorities for the next generation of IDH-directed agents are outlined.
Rapid and reliable identification of multivariate geochemical anomalies is critical for delineating prospective mineralized zones and reducing uncertainty in mineral exploration targeting. Extended isolation forest (EIF) is a powerful unsupervised ensemble learning algorithm that efficiently isolates anomalies from high-dimensional geochemical datasets using randomly oriented hyperplane partitions. Previous studies have demonstrated the effectiveness of EIF in multivariate geochemical anomaly detection and mineral potential modeling. However, its performance can be significantly affected by stochastic variability arising from random partitioning and random subsampling during isolation tree construction, which may result in unstable anomaly patterns and inconsistent exploration targets in complex geological environments. To mitigate this limitation, we developed a robust unsupervised framework for the identification of multivariate geochemical anomalies associated with gold mineralization in the Southwestern Yilgarn Craton, Australia. The proposed framework integrates robust factor analysis (RFA), a Jaccard-based stability index and EIF to enhance the reliability and reproducibility of anomaly detection. RFA was first applied to compositional soil geochemical data to identify the most significant pathfinder elements associated with gold mineralization, which were subsequently used as input variables for the EIF model. The model was then optimized using a Jaccard-based stability criterion to ensure consistent anomaly detection across repeated independent runs. Model performance was assessed using area under the receiver operating characteristic curve (AUC). The obtained AUC value of 0.82 indicates strong predictive capacity, confirming that the generated anomaly map effectively delineates mineralization-related geochemical patterns and provides a reliable proxy for mineral prospectivity mapping. Overall, the proposed framework offers a robust and reproducible unsupervised approach for multivariate geochemical anomaly detection with strong applicability in both greenfield and brownfield mineral exploration settings.
Rare disease drug development generates fragmented evidence that often fails health technology assessment (HTA) standards. This article sets out a 'snapshots to 360-degree movies' framework: Reconstructing a disease course from patients each seen once, through autoencoders, data-driven disease staging and optimal transport. Each component has been demonstrated elsewhere-ordering in rare neurodegenerative disease, the others outside rare disease-but not yet together at patient-population scale; the article states the conditions under which it would fail. The binding constraint is not regulatory: Regulators are increasingly receptive, but HTA bodies remain the bottleneck, and the European Union's Joint Clinical Assessment has put comparative evidence on the critical path-for most rare diseases, precisely what is missing. Building that capability may become a competitive advantage.
Generative artificial intelligence (AI) is reshaping molecular design, but many outputs are still judged by proxy metrics that do not establish whether proposed compounds can be synthesized, tested and advanced. This review reframes the field around ‘synthetic reality’: the extent to which AI-generated molecules survive route planning, precursor availability, experimental execution and medicinal chemistry decision-making. We examine the limits of heuristic synthesizability metrics, assess route-aware and execution-facing systems and propose maturity levels, reporting standards, actionability metrics and decision gates for auditable real-world evaluation. Future progress will depend on shifting from molecule generation alone to experimentally actionable design.
Recent technical advances, including AI co-scientists and agentic systems, increasingly allow TechBios to undertake scientific verification at scale. In addition to facilitating internal discovery efforts, these capabilities can also be used for making external investment decisions. The emerging synergies between discovery and investment enable a new type of hybrid entity, the TechBio-Investor, which has the potential to reshape the economics and ethics of biotech investing. Scaled verification can help improve capital allocation, steering investment toward stronger science and in a sector where most drugs fail, thereby conserving resources. The same capabilities also carry risks, including the uneven distribution of benefits and the conflicts faced by TechBio-Investors that compete with, invest in and bet against rivals.
Bifunctional molecules are a leading trend in drug development today, but the lack of small-molecule ligands greatly limits their development. Within this landscape, protein tags have played a pivotal role in advancing the field, enabling work from initial proof-of-concept validation to in-depth mechanistic research and further exploration of biological effects. In this review, we systematically summarize and analyze the various protein tags employed in the development of bifunctional molecules, aiming to help researchers in the field select suitable protein tags for their studies. Furthermore, we offer a perspective on the future application of protein tags in bifunctional molecule development.
Rare-disease assets account for a substantial and growing share of biopharmaceutical development and dealmaking, and published analyses provide benchmarks for their clinical, commercial and financial performance. Whether investment and business-development practitioners hold beliefs consistent with these benchmarks is largely unexamined. We surveyed 43 professionals across venture capital, pharmaceutical and biotechnology business development, asking them to estimate benchmarks for approval probability, acquisition timing, exit economics, time to peak sales and launch performance, and compared their responses with the published literature. Respondents were generally well calibrated on some structural characteristics visible through deal flow, such as the dominant modality for rare-disease assets, but systematically miscalibrated on specific magnitudes. Launch performance was the most underestimated, with 84% placing it below published levels, and most respondents underestimating how late acquisitions of rare-disease assets occur. Estimate accuracy was not associated with years of experience. These findings suggest a wide gap between published evidence and practitioner belief, which might lead some rare-disease assets to be assessed more conservatively than historical performance would justify.
Oral drug product development has traditionally been optimized around maximizing mean bioavailability in healthy volunteers, yet clinically relevant exposure liabilities including food effects, pH sensitivity, transit dependence and drug-drug interactions often emerge only after first-in-human (FIH) studies, driving label restrictions or reformulation. We introduce Exposure Robustness by Design (ERbD) - a prospective framework that reframes formulation development from maximizing mean bioavailability toward achieving exposure robustness across the physiological variability of the intended patient population. Supported by biopredictive dissolution, physiologically based biopharmaceutics modeling and model-informed drug development, ERbD guides pre-FIH formulation decisions to support predictable, clinically translatable oral drug products.
Oligonucleotide therapeutics (ONTs) enable precise modulation of gene expression and RNA function through diverse sequence- and structure-dependent mechanisms but face translational challenges in conventional animal models, particularly for human-specific biology. This review focuses on organoids and organ-on-a-chip platforms as emerging human-relevant models within the broader framework of new approach methodologies for oligonucleotide drug development. We summarize major oligonucleotide modalities and biological barriers to cellular uptake and intracellular trafficking, emphasizing hepatic delivery and extrahepatic applications. Representative studies illustrate their use in assessing target engagement, RNA modulation, pharmacological response and safety. We further discuss regulatory considerations, context of use and current limitations. Further development of these models is expected to enhance translational relevance and support the development of ONTs.
Population heterogeneity in disease biology, drug exposure and treatment response is often addressed only after development has progressed, limiting generalizability and increasing late-stage risk. We propose an anticipate-verify-influence framework to integrate clinically relevant variability from research through development. The approach combines diverse human-derived models, real-world data, model-informed drug development and AI/ML to identify drivers of heterogeneity, test their clinical relevance early and translate validated findings into trial design, dosing and patient selection. Rather than pursuing demographic representation alone, the framework emphasizes mechanistic and quantitative understanding of intrinsic and extrinsic determinants of response. Earlier characterization of meaningful heterogeneity could improve development decisions, trial representativeness and evidence generation for patients who are frequently underrepresented.
Hepatocellular carcinoma (HCC) is the most common cancer and a leading cause of death. Multi-kinase inhibitors are vital for treating advanced HCC, yet drug resistance remains a major challenge. The molecular basis of metabolic adaptation to drug resistance is poorly understood. Evidence indicates that metabolic reprogramming, particularly dysregulated cholesterol metabolism, plays a key part in drug resistance. Sterol regulatory element-binding protein 2 (SREBP2) regulates cholesterol synthesis, stabilizes receptor tyrosine kinases (RTKs), promotes lipid raft formation, and reduces ATP-binding cassette transporter subfamily A member 1 (ABCA1) and cholesterol efflux, leading to increased intracellular cholesterol. Y-box binding protein 1 (YBX1), a DNA/RNA-binding protein, induces cancer metastasis and drug resistance by modulating ABC transporters, phosphoinositide 3-kinase (PI3K)/AKT, and lipid metabolism. This review discusses the role of cholesterol metabolism in resistance, focusing on SREBP2 and therapeutic strategies targeting cholesterol.
Water conservancy and hydropower projects enhance regional climate resilience and watershed water security yet inevitably trigger large-scale involuntary reservoir resettlement. As representative involuntarily displaced populations, reservoir resettlees' social identity directly impacts local social stability and regional sustainable socioeconomic development. Based on a ten-year longitudinal qualitative investigation including in-depth interviews and participant observation in Village Y of Wuxikou Reservoir, Jiangxi Province, this paper divides the entire resettlement process into three stages: relocation, stabilization and development. From the dual perspectives of host community identity and out-groups identity, this study explores the dynamic evolutionary rules of resettlees' social identity. The results indicate that resettlees sequentially develop alienated identity, superficial adaptive identity and segregated identity across different phases. On this basis, the core concept of differential identity is proposed, which features a dual structure of vertical temporal differentiation and horizontal spatial differentiation. This paper expands the applicable scope and explanatory power of the “differential mode of association” and social identity theory in involuntary resettlement contexts. Grounding on the differential identity framework, this paper puts forward targeted integrated governance solutions to break intergroup segregation, facilitate cross-group integration and build resilient resettlement communities consistent with Sustainable Development Goals (SDGs) 11 and 13.
The discrimination of ore deposit types is primarily based on geological, geochemical, and isotopic characteristics. Conventionally, these types are identified using specific element diagrams. However, traditional geochemical methods often fail to determine scheelite deposit types of the complex Xuefengshan Sb-Au-W metallogenic belt in China, where mineralization resulted from the superposition of multiphase geological events. Machine learning (ML) methods, have been increasingly applied to identify deposit genesis by establishing relationships between deposit characteristics and genetic types using extensive datasets. However, inaccurate data labels, the limitations of single models, and poor model interpretability lead to decreased accuracy. This study proposes a ML framework based on interpretable ensemble learning. We collects geochemical element data from typical orogenic and magmatic-hydrothermal scheelite deposits globally. Deep clustering is used to filter data and overcome the subjectivity of original data labels. An ensemble learning model is used to construct a classifier to improve the model's robustness and generalization ability. An interpretable model is introduced to analyze the contribution of individual feature elements, revealing the metallogenic genesis. This method demonstrates high accuracy on the test set. According to this method, the scheelite deposit type of the Xuefengshan metallogenic belt is primarily magmatic-hydrothermal in origin, with orogenic superposition. This helps resolve a long-standing controversy in the region and establishes a repeatable and interpretable new paradigm for ML-based discrimination of ore deposit genetic types.
Though volcanogenic massive sulfide (VMS) deposits are major global sources of indium (In), the physicochemical mechanisms and key factors controlling its significant enrichment remain poorly understood. To address the issue, this study investigates the Tiemurt VMS Pb-Zn-Cu deposit, utilizing detailed petrography, in-situ LA-ICP-MS analysis, and thermodynamic modeling to reveal the In enrichment mechanisms in VMS deposits. Petrographic observations identified two distinct generations of sphalerite corresponding to different mineralization stages. The early-stage sphalerite (Sp1) is euhedral-subhedral, associated with pyrite, and displays darker colors (red to brown), whereas the late-stage sphalerite (Sp2) is anhedral, intimately intergrown with chalcopyrite, and shows lighter colors (mainly yellow). The trace element results demonstrate that Sp1 has a significantly higher In content (average 317 ppm) than Sp2 (average 220 ppm). Additionally, In concentrations positively correlate with Fe contents. Because Fe is the primary chromophore that darkens sphalerite, this strong coupled enrichment mechanism allows macroscopic sphalerite color (red > brown > yellow) to serve as a reliable indicator for In concentration. Crystallization temperatures calculated using the GGIMFis thermometer range from 344 to 382 °C for Sp1 and 312 to 355 °C for Sp2, indicating a cooling trend during fluid evolution. Thermodynamic modeling data showed that in the early-stage hydrothermal fluids (≥360 °C), Zn2+ preferentially complexes with Cl−, leaving InCl2+ or In3+ as unstable species, and In efficiently precipitates into Sp1 under the environment of log fO2 = −32 to −26 and pH = 6–8. As the fluids cool at ~340 °C, weakened Zn2+ competition allows In3+ to form stable InCl3, and In precipitates into Sp2 under the conditions of log fO2 = −42 to −32 and pH = 5.5–11. We therefore conclude that the key factor controlling the difference in In content between Sp1 and Sp2 is the precipitation mechanism rather than migration capacity. This may be different from the In enrichment mechanism associated with magmatic hydrothermal systems, where In is mainly present as InCl3 complexes with strong migration capacity. These new findings enable us to understand how the physicochemical conditions of fluids control the enrichment of In in VMS deposits, and also highlight that the color of sphalerite can be used to target potential In resources in PbZn deposits.
In a selective global biotech funding environment characterized by a 'flight to quality', startups must signal execution readiness to secure capital. Based on a dataset of Japanese drug discovery startups, we examine how management teams' professional and educational backgrounds relate to fundraising performance, finding stage-dependent patterns. Early-stage startups are predominantly academic-led, yet raise less than those led by pharmaceutical veterans. In later stages, CEOs with purely academic backgrounds are associated with lower funding, whereas CEOs with medical degrees or pharmaceutical experience are associated with higher funding. Retaining academic founder-CEOs early might also introduce governance and conflict-of-interest risks. These results are consistent with a view that leadership should evolve as firms mature-incorporating industry professionals early and, potentially, transitioning founder-CEOs before late-stage transitions.
Epigenomic dysregulation is associated with several noncommunicable diseases (NCDs). Chromatin-modifying drugs have the ability to re-establish normal epigenetic regulation, thereby fine-tuning the epigenome. The biochemical and pharmacological mechanisms of these drugs, which target histone lysine and arginine methyltransferases, DNA methyltransferases, histone deacetylases and histone acetyltransferases, histone demethylases and bromodomain and extra-terminal motifs, serve as ideal tools for understanding the modes of action of these epigenetic regulators and the resulting biological events within chromatin. The present review provides an overview of these biochemical mechanisms and some downstream effects of the aforementioned chromatin-modifying drugs.
Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have transformed the management of type 2 diabetes and obesity and are increasingly recognised for their pleiotropic effects beyond glycaemic control. Growing evidence suggests that these agents influence inflammatory, vascular and neuroprotective pathways relevant to ocular disease, creating opportunities for therapeutic repurposing while raising important questions regarding long-term ocular safety. This review crucially synthesises current mechanistic, preclinical and clinical evidence regarding the role of GLP-1 RAs across major ophthalmic conditions. Experimental studies consistently demonstrate anti-inflammatory, antioxidative and neurovascular-protective effects, particularly in diabetic retinopathy, whereas clinical studies suggest a potential benefit yet are limited by heterogeneous study designs and the absence of dedicated ophthalmic endpoints. We also examine emerging safety signals, including reported associations with nonarteritic anterior ischaemic optic neuropathy and neovascular age-related macular degeneration, highlighting the limitations of current observational evidence and the uncertainty surrounding causality. Finally, we discuss the translational potential of GLP-1 RAs in immune-mediated ocular diseases such as noninfectious uveitis and identify key priorities for future research. Because the clinical use of GLP-1 RAs continues to expand, carefully designed prospective studies integrating mechanistic insights with standardised ophthalmic outcomes will be essential to define both their therapeutic potential and long-term ocular safety.
