Digital Transformation in Toxicology: Improving Communication and Efficiency in Risk Assessment

Abstract

Toxicology is undergoing a digital revolution, with mobile apps, sensors, artificial intelligence (AI), and machine learning enabling better record-keeping, data analysis, and risk assessment. Additionally, computational toxicology and digital risk assessment have led to more accurate predictions of chemical hazards, reducing the burden of laboratory studies. Blockchain technology is emerging as a promising approach to increase transparency, particularly in the management and processing of genomic data related with food safety. Robotics, smart agriculture, and smart food and feedstock offer new opportunities for collecting, analyzing, and evaluating data, while wearable devices can predict toxicity and monitor health-related issues. The review article focuses on the potential of digital technologies to improve risk assessment and public health in the field of toxicology. By examining key topics such as blockchain technology, smoking toxicology, wearable sensors, and food security, this article provides an overview of how digitalization is influencing toxicology. As well as highlighting future directions for research, this article demonstrates how emerging technologies can enhance risk assessment communication and efficiency. The integration of digital technologies has revolutionized toxicology and has great potential for improving risk assessment and promoting public health.

Figure 1

Timeline of digitalization showing invention of transistors in 1947, commercialization of mobile phones in 1979, founding worldwide web in 1989, the advent of 3rd generation mobiles with digital texting and sign feature in 2001, Facebook and Apple iPhone launch in 2006 and 2007, respectively, India launches the cheapest smartphone in 2016, and spread of digitalization in 2020 due to pandemic led virtual collaborative era. The latest revolution ChatGPT plays a crucial role in digitalization by providing human-like language capabilities to machines, enabling them to understand and communicate with users in natural language, thus facilitating the automation of various tasks and interactions.

Figure 2

Digitalization of food and feed classification for tracing purposes where integration of next-generation sequencing (NGS), machine learning, and data mining can provide traceability to consumers from farm to table. The food products have a tag describing their origin, contents, manufacturing details, etc. which could enhance transparency and tracing by consumer in food safety.

Figure 3

Digitalization toward smoking toxicology. Electronic cigarettes (e-cigarettes) and e-liquids produce carcinogenic aerosols by heating a certain liquid that contains nicotine, are used as an alternative to regular cigarettes, which can be self-monitored via adds-on devices with mobile apps, which can provide deeper insight into ill-effect via communicating computational databases (1). The heated nicotine products cause Vaping-associated pulmonary injury (VAPI) or vaping product use-associated lung injury (EVALI) and may destroy beneficial microflora causing opportunistic infection (2). Digital toxicology further enables laboratory diagnosis and radiological interventions in clinics upon confirmation of toxicological symptoms (3). Created with BioRender.com.

Figure 4

Digitalization in toxicology using hand-held devices, mobile apps, and wearable devices. Wearable devices monitor and track fitness, blood pressure, and blood sugar levels. Wearable biosensors are wearable devices that allow patients to collect data on their movement, heart rate, respiratory rate, and temperature and can reduce and prevent chances of cardiac and respiratory arrest, while hand-held devices can keep records of patients and their prescriptions, demonstrate help to patients by a personal digital assistant (PDA), keep track of patients’ health, and eliminate delay or error on patients’ end. Certain mobile apps can also help in tracking fitness, setting reminders for medications, offering self-diagnosis solutions, checking in on the mental health of the patients, and much more. These digital tools collect, analyze, and evaluate the data acquired to monitor toxicological effects.

Figure 5

Digital toxicology in handling patient ‘OMICS’ data and storage to monitor the health and detect toxińs response. Digital OMICS to provide actionable knowledge across molecular medicine, diagnostics, and epidemiology. Hypothesis-driven data generation knowledge engineering from personalized medicine will enable digitalization interfacing Environmental and industrial toxicology; clinical and regulatory toxicology; Forensic molecular toxicology (Created with BioRender.com).

Figure 6

Blockchain technology in toxicology can aid in decentralized data keeping and evaluate data that and potential risks to consumers with exposure risks through available chemical databases. MSDS (Material Safety Datasheet) contains information about potential hazard-causing agents and how to work with them. Blockchain also involves traceability in the distribution network and the use of product barcodes in manufacturing. Collection, analysis, and evaluation of the data in lab testing and pharmacies are possible due to blockchain (Created with BioRender.com).

Figure 7

Blockchain in genomic toxicology. Digital gene interactions by Synthetic Gene array and storage of quantitative genetic interaction scores in a blockchain data repository. The query genes are extracted from toxin-led mutants to determine genetic interaction with the ones from the original genome. The images of an array are taken by a digital camera and these image stacks are quantified and a raw database is created. The correction in this raw data with respect to different effects leads to genetic interaction scores which are stored in blockchain data repositories of genomic toxicology (Created with BioRender.com).

Figure 8

An envisioned future digital platform that would be designed to monitor and track the presence of nanomaterials in consumer products, and to take regulatory measures to ensure that these products are safe for human use. This type of platform would likely require the integration of several different submodules, including ones for pollutant generation and screening, data production and sharing, and communication with regulatory agencies and law enforcement. It would also likely involve the use of advanced computational technologies to analyze and interpret the data collected and to identify potential risks or hazards associated with the use of certain products. Reproduced with permission from ref (3). Copyright 2018, Wiley.