Next-Generation Biomarkers: Redefining Diagnostic Precision in Toxicology Volume 65- Issue 2

Nahlah A Alqriqri¹, Nada D Alzahrani², Kholoud T Ashour³, Rasha F Zakariya⁴, Asia Alharbi⁵, Maha Algregory⁶, Maha A Rummani⁷, Muhammad Abdullah Ismail⁸*, Ahmed Omer Faroqi Syed Mazher Hussain⁹, Majed Saeed Alzahrani¹⁰, Jawaher Mesfer Alyami¹¹, Abdulrahman Lutfi Kalantan¹² and Fouad Mohammed Sait¹³

• ¹King Abdulaziz University Hospital - Jeddah, Saudi Arabia
• ²Medical Laboratory Technologist, King Abdulaziz University Hospital - Jeddah, Saudi Arabia
• ³Medical Laboratory Technologist, King Abdulaziz University Hospital - Jeddah, Saudi Arabia
• ⁴Medical Laboratory Technologist, King Abdulaziz University Hospital - Jeddah, Saudi Arabia
• ⁵King Abdulaziz University Hospital - Jeddah, Saudi Arabia
• ⁶Regenerative Medicine Unit, King Fahd Medical Research Center, King Abdulaziz University - Jeddah, Saudi Arabia
• ⁷King Abdulaziz University Hospital - Jeddah, Saudi Arabia
• ⁸Medical Laboratory Technologist, King Abdullah Medical City – Makkah, Saudi Arabia
• ⁹Specialist – Nursing, King Abdullah Medical City – Makkah, Saudi Arabia
• ¹⁰Medical laboratory technologist, King Abdulaziz University Hospital - Jeddah, Saudi Arabia
• ¹¹Medical laboratory technologist, King Abdulaziz University Hospital - Jeddah, Saudi Arabia
• ¹²Medical Laboratory Technologist, King Abdulaziz University Hospital - Jeddah, Saudi Arabia
• ¹³King Abdulaziz University Medical Services Center, Saudi Arabia

Received: February 22, 2026; Published: March 30, 2026

*Corresponding author: Muhammad Abdullah Ismail, Medical Laboratory Technologist, King Abdullah Medical City – Makkah, Saudi Arabia

DOI: 10.26717/BJSTR.2026.65.010156

Abstract PDF

The evolution of toxicological science is currently undergoing a paradigm shift, transitioning from traditional descriptive observations toward a mechanism-based predictive framework. As conventional diagnostic tools often fail to capture early-stage molecular perturbations, there is an urgent need to integrate advanced biological indicators into clinical and environmental safety assessments. This narrative review aims to evaluate the emergence of next-generation biomarkers and their role in enhancing diagnostic precision within toxicology. Utilizing a narrative research design, the study synthesizes current literature to analyze the integration of “omics” technologies, Artificial Intelligence (AI), and novel sampling techniques. The major results indicate that next-generation biomarkers—including liquid biopsies, epigenetic markers, and digital signatures—offer superior sensitivity and specificity compared to classical parameters like serum creatinine or liver enzymes. Furthermore, the synergy between big data and machine learning is found to be essential for establishing precise reference intervals and predicting wellness-to-disease transitions. The review recommends the standardization of bioanalytical validation protocols and the cross-species translation of biomarkers to improve regulatory acceptance. Conclusively, the integration of these advanced tools redefines toxicological precision, shifting the focus from reactive diagnosis to proactive, personalized risk management. The implications of this shift are profound, suggesting a future where toxicological monitoring is continuous, non-invasive, and highly individualized.

Keywords: Next-Generation Biomarkers, Precision Toxicology, Artificial Intelligence, Risk Assessment, Omics Technologies, Diagnostic Precision.

Abbreviations: AI: Artificial Intelligence; NGS: Next-Generation Sequencing; NAMs: New Approach Methodologies; ALT: Alanine Aminotransferase

The landscape of modern toxicology is being fundamentally reshaped by the convergence of high-throughput technologies and computational intelligence, moving beyond the limitations of traditional endpoint assessments (Addissouky, et al. [1]). Historically, toxicological evaluations relied heavily on animal models and histopathological changes that often manifested only after significant organ damage had occurred (Sheng, et al. [2]). However, the emergence of next-generation biomarkers represents a transformative shift toward early detection and molecular precision (Alnasser, et al. [3]). These advanced biological indicators are essential for identifying subtle physiological shifts long before clinical symptoms emerge (Rappaport, et al. [4]). In the current era of precision medicine, the ability to quantify these shifts is critical for both drug development and environmental safety (Yildirim, et al. [5]). The development of the translational safety biomarker pipeline has highlighted the necessity of moving toward biomarkers that possess high diagnostic and prognostic value (Grove, et al. [6]). Standard markers of toxicity, such as those used for drug-induced liver injury, frequently lack the specificity required for modern clinical trials (Grove, et al. [6]). Consequently, there is a growing emphasis on New Approach Methodologies (NAMs) that integrate in vitro data with human-relevant molecular markers (Sheng, et al. [2]). This integration is particularly vital in genetic toxicology, where revisiting DNA damage detection methods is necessary to meet evolving regulatory implications (Alnasser, et al. [3]).

Furthermore, the application of genomics and bioinformatics has accelerated the discovery of diagnostic biomarkers that can be validated across diverse populations (Dubey, et al. [7]). In the realm of oncology and systemic toxicity, next-generation sequencing (NGS) has emerged as a cornerstone for identifying resistance management markers and therapeutic targets (Isaic, et al. [8]). The transition from generalized treatments to personalized molecular-targeted therapies is heavily dependent on the unlocking of these biomarker powerhouses (Molla & Bitew, et al. [9]). This shift is not confined to cancer but extends to the management of infectious diseases and the monitoring of environmental pollutants (Manolescu, et al. [10,11]). For instance, the use of exosomes as liquid biopsies offers a minimally invasive window into the cellular microenvironment, providing real- time data on toxicity and disease progression (Youssef, et al. [12]). Such innovations allow for a more nuanced understanding of how external stressors interact with internal biological systems (Dasgupta, et al. [13]). The role of artificial intelligence in this ecosystem cannot be overstated, as AI ecosystems are now deployed for real-time biosurveillance and dynamic intervention planning (Amanna, et al. [14]). By analyzing complex datasets, AI helps in redefining the boundaries of wellness and disease, facilitating a move toward precision health analytics (Rappaport, et al. [4]).

This is particularly relevant in specialized fields like nephrotoxicity, where novel biomarkers following specific poisonings are being identified to provide more accurate clinical outcomes (Shihana, et al. [15]). Moreover, the use of nanotechnology, such as nanogold-albumin conjugates, is enhancing the sensitivity of diagnostic tools, allowing for the detection of biomarkers at ultra-low concentrations (Jaiswal, et al. [16]). These advancements collectively suggest that the future of toxicology lies in a multi-modal approach that combines biological insights with technological prowess (Khan, et al. [17]). As we look toward the horizon of 2026, the integration of wearable optical sensors and point-of-care diagnostics is making personalized health monitoring a reality (Duarah, et al. [18]). These tools enable the continuous tracking of biomarkers, providing a longitudinal view of an individual’s toxicological profile (Duarah, et al. [18]). Furthermore, the exploration of the tumor microenvironment and immuno-engineering is revealing new layers of complexity in how toxins influence long-term health (Alamri, et al. [19]). In multiple myeloma and other complex pathologies, next-generation biomarkers are already advancing risk stratification beyond current guidelines (Lopes, et al. [20]). The synergy between nanotechnology and biomarker identification is thus setting a new standard for early detection (Salaudeen, et al. [21]). Ultimately, these innovations aim to foster a safer environment and more effective clinical interventions (Prasanth, et al. [22]). The purpose of this review is to evaluate how next-generation biomarkers are redefining diagnostic precision in toxicology by integrating omics, AI, and novel detection technologies (AlDoughaim, et al. [23]).

The current infrastructure for establishing reference intervals in clinical toxicology and laboratory medicine is significantly flawed, relying on outdated static models that fail to account for individual biological variability (Addissouky, et al. [1]). While the “Intelligent Laboratory” concept promises to revolutionize this field by synergizing Laboratory Information System (LIS) Big Data with Artificial Intelligence (AI), the practical implementation faces substantial hurdles (Amanna, et al. [14]). Existing reference intervals are often derived from small, homogenous populations, which do not reflect the diversity found in global clinical settings (Grove, et al. [6]). This lack of precision leads to misinterpretation of toxicological data, potentially resulting in missed diagnoses or unnecessary medical interventions (Sheng, et al. [2]). Furthermore, the transition from “wellness” to “disease” is a fluid process that traditional laboratory thresholds are illequipped to capture (Rappaport, et al. [4]). A primary problem lies in the fragmentation of data within LIS, where vast amounts of potentially transformative information remain siloed and underutilized (Amanna, et al. [14]). Without the integration of AI-driven analytics, the sheer volume of “Big Data” becomes an obstacle rather than an asset, obscuring critical patterns in biomarker fluctuation (Dubey, et al. [7]). In toxicology, this is particularly dangerous, as early-stage DNA damage or subtle nephrotoxic changes may be overlooked because they fall within “normal” but non-personalized ranges (Alnasser, et al. [3,15]).

The reliance on conventional biomarkers like creatinine or transaminases persists despite their known lack of sensitivity in early- phase toxicity (Salama, et al. [11]). Moreover, there is a critical gap in the validation of AI algorithms designed to establish these precision reference intervals (Yildirimm, et al. [5]). Many AI models function as “black boxes,” lacking the transparency required for clinical and regulatory acceptance in toxicological risk assessment (Sheng, et al. [2]). This issue is compounded by the rapid emergence of next-generation sequencing and phenomics data, which provide a wealth of information that current LIS structures cannot process effectively (Isaic, et al. [8,13]). Consequently, the disconnect between high-tech biomarker discovery and low-tech clinical implementation remains a significant barrier (Prasanth, et al. [22]). The problem is further exacerbated by the slow adoption of New Approach Methodologies (NAMs) in standardized clinical practice (Sheng, et al. [2]). While research emphasizes the power of personalized molecular-targeted therapies, the diagnostic framework supporting these therapies remains rooted in 20th-century methodologies (Molla & Bitew, et al. [9]). There is an urgent need to address how LIS Big Data can be harmonized with AI to create dynamic, real-time reference intervals that adapt to the patient’s unique physiological context (AlDoughaim, et al. [23]). Failure to bridge this gap means that the potential of precision medicine will remain unrealized for the vast majority of patients (Khan, et al. [17]). Therefore, this review addresses the critical necessity of integrating AI and LIS Big Data to redefine diagnostic precision and safety pharmacology (Addissouky, et al. [1]).

1. To evaluate the efficacy of AI-driven algorithms in processing LIS Big Data for the establishment of personalized toxicological reference intervals.

2. To analyze the role of next-generation biomarkers, such as exosomes and circulating DNA, in enhancing early toxicity detection compared to traditional parameters.

3. To investigate the challenges of integrating New Approach Methodologies (NAMs) into current clinical laboratory workflows for real-time biosurveillance.

The paradigm of the “Intelligent Laboratory” represents a shift toward the data-centric management of human health, where the synergy of LIS Big Data and AI facilitates precision reference interval establishment (Amanna, et al. [14]). Traditional toxicology has long been criticized for its reliance on population-based averages which ignore the specific genetic and environmental context of the individual Salama, et al. [11]). Next-generation biomarkers are now being utilized to bridge this gap, offering a more granular view of cellular response to toxic insults (Addissouky, et al. [1]). These biomarkers include diverse molecular species, such as microRNAs, epigenetic modifications, and volatile organic compounds (Alnasser, et al. [3]). The integration of genomics and bioinformatics is central to this evolution, allowing for the validation of these markers in large-scale datasets (Dubey, et al. 2025). Central to the “Intelligent Laboratory” theory is the concept of wellness-to-disease transitions, which posits that health is a continuum rather than a binary state (Rappaport, et al. [4]). AI ecosystems are uniquely capable of identifying the non-linear patterns that characterize these transitions (Amanna, et al. [14]). By utilizing machine learning to analyze historical LIS data, laboratories can move away from rigid, one-size-fits-all reference ranges (Yildirim, et al. [5]). For example, in the context of drug-induced liver injury, the TransBioLine consortium is working to develop safety biomarkers that provide earlier warnings than traditional enzymes (Grove, et al. [6]).

This requires the processing of complex biological signals that can only be interpreted through advanced computational models (Sheng, et al. [2]). The use of next-generation sequencing (NGS) has also revolutionized the diagnostic landscape, particularly in oncology and genetic toxicology (Isaic, et al. [8]). NGS allows for the comprehensive profiling of DNA damage, providing insights that were previously unattainable with older methods (Alnasser, et al. [3]). This is complemented by the rise of phenomics, which seeks to map the entire set of observable traits resulting from the interaction of an individual’s genes and their environment (Dasgupta, et al. [15]). In lung cancer and other systemic toxicities, phenomic approaches are helping to unravel the complexity of disease progression (Dasgupta, et al. [15]). Such detailed molecular mapping is essential for the development of targeted therapies and the personalization of treatment strategies (Molla & Bitew, et al. [9,17]). Nanotechnology further enhances this diagnostic precision by providing novel platforms for biomarker identification (Salaudeen & Akinniranye [21]). Nanogold-albumin conjugates and other nano-engineered materials allow for the detection of biomarkers in biofluids with unprecedented sensitivity (Jaiswal, et al. [16]). These technological advancements are being integrated into point-of-care devices and wearable sensors, enabling continuous health monitoring (Duarah, et al. [18]).

Wearable optical sensors, in particular, represent a breakthrough in personalized health, as they can track physiological parameters in real-time and alert users to potential toxic exposures (Duarah, et al. [18]). This shift toward real-time biosurveillance is a key component of the modern intelligent laboratory (Amanna, et al. [14]). However, the transition to an AI-driven laboratory is not without its challenges. Scholars have pointed out that the diversity of data formats and the lack of standardization across different LIS platforms hinder the collective elaboration of findings (Manolescu, et al. [10]). There is also the significant issue of nephrotoxicity markers, which must be carefully validated in specific clinical scenarios, such as following pesticide exposure (Shihana, et al. [15]). The integration of New Approach Methodologies (NAMs) into regulatory frameworks is another hurdle that requires international cooperation and robust scientific evidence (Sheng, et al. [2]). Despite these obstacles, the consensus among researchers is that the future of toxicology lies in the successful fusion of biological discovery and digital innovation (AlDoughaim, et al. [23]). This review highlights that the synergy between these fields is the only way to achieve true precision in diagnostic and therapeutic outcomes (Prasanth, et al. [20,22]).

The results of this review demonstrate that AI-driven algorithms are highly effective at identifying subtle trends within LIS Big Data that are invisible to traditional statistical methods (Amanna, et al. [14]). By applying machine learning to millions of data points, researchers have successfully established personalized reference intervals that significantly reduce the rate of false positives and negatives in toxicological screening (Yildirim, et al. [5]). These AI models are particularly adept at recognizing the “wellness-to-disease” signatures that precede clinical pathology (Rappaport, et al. [4]). Furthermore, the integration of bioinformatics has allowed for the cross-validation of these intervals across different demographic groups, enhancing the global applicability of diagnostic findings (Dubey, et al. [7]). Regarding the second objective, next-generation biomarkers such as exosomes and liquid biopsies have proven to be far more sensitive than traditional biochemical markers (Youssef, et al. [12]). In cases of drug-induced liver injury, these novel markers provide evidence of cellular stress days before serum alanine aminotransferase (ALT) levels rise (Grove, et al. [6]). Similarly, in genetic toxicology, NGS-based DNA damage detection offers a much higher resolution of genomic instability compared to the classic micronucleus assay (Alnasser, et al. [3]). These advanced markers also facilitate the monitoring of environmental pollutants, allowing for the detection of toxic effects at much lower exposure levels (Salama, et al. [11]).

The investigation into NAMs integration revealed that while the technology exists, the primary barrier is the lack of standardized regulatory protocols (Sheng, et al. [2]). However, where these methodologies have been implemented—such as in “clinical trials in a dish” using iPSCs—they have successfully predicted human-specific toxicities that animal models missed (Yildirim, et al. [5]). The results also show that nanotechnology-enhanced diagnostics are now capable of identifying early cancer biomarkers with a level of precision that was previously impossible (Salaudeen, et al. [21]). This synergy between nanotechnology, AI, and LIS data is creating a new ecosystem for real- time biosurveillance and personalized health analytics (Amanna, et al. [14,23]).

The results of this narrative review indicate a fundamental shift in the diagnostic landscape of toxicology, where the “Intelligent Laboratory” serves as the nexus for precision medicine (Amanna, et al. [14]). The superior performance of AI-driven reference intervals over static population averages highlights the critical need to move toward dynamic, patient-centric models (Yildirim, et al. [5]). This is not merely a technical upgrade; it is an interpretive revolution that allows clinicians to understand a patient’s current health status in the context of their own historical data (Rappaport, et al. [4]). The ability of AI to detect wellness-to-disease transitions suggests that we can now intervene in toxic processes much earlier than previously thought possible (Rappaport, et al. [1,4]). The implications of using next-generation biomarkers like exosomes and NGS are far-reaching (Youssef, et al. [8,12]). These tools allow for the “phenotyping” of toxicity, where the specific mechanism of damage can be identified and targeted with molecular therapies (Dasgupta, et al. [9,15]). This is a significant improvement over traditional toxicology, which often identified that damage had occurred but could not always explain the underlying pathway (Sheng, et al. [2]). By integrating these markers with nanotech- based sensing, we are moving toward a future of “invisible” diagnostics, where health is monitored continuously through wearables without the need for invasive sampling (Duarah, et al. [16,18]).

However, the “black box” nature of AI remains a point of critical concern (Amanna, et al. [14]). For these systems to be fully integrated into clinical workflows, there must be a focus on “explainable AI” that provides transparent rationales for its diagnostic suggestions (Sheng, et al. [2]). Furthermore, the clinical significance of many next-generation biomarkers still requires extensive longitudinal validation (Grove, et al. [6]). The gap between discovering a biomarker and its regulatory approval for use in safety pharmacology remains a significant bottleneck (Addissouky, et al. [1]). Addressing these challenges will require a multidisciplinary approach that combines toxicology, data science, and ethics (Alnasser, et al. [3,22]).

This review has demonstrated that next-generation biomarkers and AI-integrated laboratory systems are fundamentally redefining diagnostic precision in toxicology. The move from static reference intervals to dynamic, personalized profiles—fueled by LIS Big Data— allows for the detection of subclinical toxicities and wellness-to-disease transitions with unprecedented accuracy. Technologies such as next-generation sequencing, liquid biopsies, and nanotechnology-enhanced sensors provide a level of molecular detail that traditional parameters cannot match. While the transition to this “Intelligent Laboratory” framework is complicated by regulatory and technical hurdles, the potential for improving patient safety and therapeutic outcomes is immense. Ultimately, the integration of these advanced methodologies ensures that toxicological assessment is no longer just a reactive science, but a proactive pillar of precision medicine. To bridge the gap between theoretical biomarker discovery and practical clinical application, it is imperative to establish standardized, transparent frameworks for AI algorithms used in LIS data analysis. Ensuring that these computational models operate within “explainable AI” parameters will facilitate clinical reliability and pave the way for formal regulatory approval. Furthermore, the scientific community must prioritize large-scale, longitudinal studies to confirm the longterm prognostic value of emerging biomarkers, such as exosomes and epigenetic modifications, across diverse and globally representative populations.

The evolution of toxicological safety also necessitates the accelerated adoption of New Approach Methodologies (NAMs), including organ- on-a-chip and iPSC models, to complement and eventually reduce reliance on traditional animal testing. Simultaneously, clinical laboratory infrastructures should be modernized to incorporate real-time data from wearable optical sensors, allowing for the continuous monitoring of toxicological profiles in “at-risk” individuals. Finally, fostering interdisciplinary expertise is essential; academic curricula and professional fellowships should integrate data science and bioinformatics to equip future toxicologists with the skills required to navigate the digital transformation of the intelligent laboratory.

While traditional models focus on the former, the PRDX3-ferroptosis- GP axis compellingly argues for the critical importance of the latter’s viability. Future research must rigorously validate this axis in vivo, utilizing specific genetic and pharmacological models to establish causality and delineate the upstream triggers (e.g., chronic inflammation, metabolic stress) and downstream effectors linking GP ferroptosis to conduction system depression. Ultimately, targeting this pathway—via ferroptosis inhibitors or PRDX3 activators—holds significant therapeutic promise. It offers a novel strategic avenue for the precision medicine of neurogenic bradyarrhythmias, potentially complementing or providing alternatives to electronic pacemaker therapy. However, a balanced approach is essential; interventions must be carefully evaluated for specificity to avoid unintended systemic consequences of modulating a fundamental cell death pathway. The journey from mechanistic insight to clinical application will require robust translational studies, but the exploration of the PRDX3-ferroptosis nexus undeniably opens a new frontier in understanding and treating rhythm disorders of neural origin.

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