Abstract
Digital technologies are omnipresent and have influenced the entire continuum of preclinical research activities both in high-income countries and low-and middle-income countries. This chapter describes the many different aspects of preclinical research and how it is affected by digitization. These include the digital tools and technologies in preclinical research, the ways in which digitization enhances data management in preclinical research, as well as experimental design in preclinical research.
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Keywords
- Preclinical research
- Digital health
- Electronic data capture
- Data integration
- Laboratory information management systems (LIMS)
- Machine learning
1 Introduction
Digitization has been transforming various fields, and preclinical research is no exception. The use of digital technologies in preclinical research has become increasingly common and has had a significant impact on the way research is conducted, managed, and analyzed. Preclinical research refers to scientific investigations conducted in the laboratory or animals before testing new therapies or interventions in humans. It encompasses a wide range of studies aimed at understanding the safety, efficacy, and mechanisms of action of potential treatments.
Digital technologies are playing an increasingly significant role in transforming preclinical research methodologies. These technologies offer a wide range of tools and approaches that enhance data collection, integration, and analysis. By integrating diverse data sources, such as genomics, proteomics, other -omics, and imaging data, researchers can gain deeper insights into disease mechanisms and treatment responses. Moreover, digital tools enable in silico approaches, virtual screening, and computational modeling, accelerating the drug discovery and optimization process. Adopting digital technologies in preclinical research holds great promise for improving efficiency, reproducibility, and the discovery of novel therapeutics.
As preclinical research consists of multiple complex topics and sub-areas, within this chapter, it will be divided it into the following fields: dedicated to Drug Discovery and Development, Disease Modeling and Pathophysiology, Safety and Toxicology Assessments, and Pharmacokinetic-Pharmacodynamic modeling and analysis.
Preclinical research consists of a series of labor-extensive steps and requires a thoughtful analysis to identify and optimize lead compounds with desirable properties for subsequent evaluation in clinical trials. The preclinical drug discovery may vary based on the specific therapeutic area, target, and research objectives, but a general, structured framework outlining the key components can be presented with the following phases (Mahajan et al. 2020; Cook et al. 2014; Vogel and Maas 2016; Li and Wang 2019):
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I.
Target Identification and Validation.
Preclinical drug discovery begins with the identification and validation of a target molecule(s) or pathway(s) that plays a critical role in the disease process. This involves understanding the underlying biology of the disease and establishing the relevance of the target in the context of the therapeutic goal.
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Hit Identification and Lead Optimization.
Once a target is identified and validated, the focus shifts to finding potential drug candidates or “hits” that interact with the target. High-throughput screening (HTS) and virtual screening are commonly employed to identify compounds that show activity against the target. Subsequently, lead optimization involves refining and modifying the initial hits to improve their potency, selectivity, and other desirable properties.
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In vitro and In vivo Testing.
Preclinical drug discovery involves extensive in vitro and in vivo testing to evaluate the pharmacological effects, efficacy, safety, and toxicity of the lead compounds. In vitro assays and cell-based models are employed to assess target engagement, cellular activity, and initial toxicity screening. Animal models, such as rodents or non-human primates, are used to assess the pharmacokinetics, efficacy, and safety of lead compounds in a more complex biological setting.
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Pharmacokinetics and Pharmacodynamics Modeling and Analysis. Understanding the pharmacokinetic and pharmacodynamic properties of lead compounds is critical in preclinical drug discovery. The pharmacokinetic properties of lead compounds are evaluated, including absorption, distribution, metabolism, and excretion (ADME). The pharmacodynamics are focused on the analysis of how lead compounds interact with the target and produce the desired effects
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Safety and Toxicology Assessment.
Preclinical drug discovery includes comprehensive safety and toxicology assessments to evaluate the potential adverse effects of lead compounds. This involves conducting a range of toxicity studies, including acute toxicity, repeated-dose toxicity, genotoxicity, and safety pharmacology assessments, to identify potential risks or side effects associated with the compounds.
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Data Analysis and Decision-making.
Throughout the preclinical drug discovery process, data analysis plays a vital role in informing decision-making. Researchers analyze and interpret the data generated from various experiments and assays to assess the efficacy, safety, and suitability of lead compounds for further development.
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Regulatory Requirements.
While preclinical drug discovery primarily focuses on understanding the properties and effects of lead compounds, there is also an awareness of regulatory requirements and considerations. Preclinical data generated during this phase lay the foundation for regulatory submissions and serve as a basis for advancing compounds into clinical trials.
Digitization plays a significant role in accelerating the drug discovery and development processes. By leveraging digital tools and technologies, researchers can streamline data collection, analysis, and collaboration, leading to more efficient identification of potential drug targets and optimization of lead compounds. Additionally, digitization enables virtual screening, computational modeling, and data-driven decision-making, expediting the design and evaluation of drug candidates, ultimately reducing costs and time-to-market for new therapies.
2 Digital Tools and Technologies in Preclinical Research
2.1 Overview of Key Digital Tools and Technologies Utilized in Preclinical Research
In the context of data management in preclinical research, digitization refers to the adoption and integration of digital technologies and tools to transform how data is collected, stored, analyzed, and shared. It involves leveraging digital platforms, software applications, and automated processes to streamline data management workflows, enhance data quality, improve accessibility, and enable more efficient and informed decision-making.
2.2 Electronic Data Capture (EDC) Systems for Efficient Data Collection and Management
Digitization in preclinical research data management encompasses several key aspects, including the fundamental one—Electronic Data Capture (EDC). Thanks to digitization, traditional paper-based data collection methods can be replaced, partly or fully, with electronic data capture systems. This allows for the direct entry of data into electronic forms or databases, reducing errors, improving data quality, and enabling real-time data available for analysis.
EDC systems eliminate the need for manual data entry from paper forms, reducing transcription errors and saving time. Researchers can directly enter data into electronic forms or databases, streamlining the data collection process. Moreover, EDC systems provide built-in data validation checks and data range validations, minimizing errors and ensuring data integrity. Required fields, data format checks, and logical checks can be implemented in electronic forms, prompting researchers to enter accurate and complete data. This helps maintain high-quality data for analysis and decision-making. With EDC systems, data is instantly available for analysis and monitoring to all those that have received the relevant regulatory and ethical clearance. Researchers can access real-time data and track study progress, enabling timely decision-making and intervention as and when required. This real-time access to data improves the overall efficiency of preclinical research studies and helps in identifying trends or issues promptly. EDC systems enable remote data capture, allowing researchers to collect data from multiple sites or participants located in different geographical locations. This flexibility eliminates the need for physical visits or manual data transfer (partly or fully), reducing logistical challenges and potential errors associated with data collection.
Data security and privacy play a pivotal role during extensive and longitudinal preclinical studies, EDC systems offer robust security measures to protect sensitive research data through access controls, encryption, and audit. These systems adhere to regulatory guidelines, such as the Health Insurance Portability and Accountability Act (HIPAA) and Good Clinical Practice (GCP) standards.
2.3 Laboratory Information Management Systems (LIMS) for Streamlined Data Organization and Integration (Cucoranu 2016; Brusniak et al. 2019; Kumuthini et al. 2020)
Digital platforms that are fundamental to managing and organizing various aspects of laboratory operations and data were developed as Laboratory Information Management Systems (LIMS). LIMS facilitates the management of preclinical research data within laboratory settings through tracking of samples, recording of experimental data, management of protocols and workflows, and integration of data from various instruments and sources. They provide a centralized and standardized approach to data management, ensuring consistency and efficient retrieval of information.
There is a wide range of LIMS platforms available on the market with a broad range of customization options according to the end-users’ needs. The LIMS systems are designed to be adaptable and extendable, making them suitable for both small and large research laboratories where multidisciplinary scientists collaborate on the engineering of compounds for potential therapeutic applications from creation to preclinical experiments.
What is important, LIMS allows researchers to track and manage samples throughout their lifecycle, from acquisition to disposal. It provides features to record sample details, such as storage location, sample type, and associated metadata. By automating sample tracking, LIMS minimizes errors, improves traceability, and facilitates easy retrieval of samples when needed.
Recording and storing experimental data generated during preclinical research within LIMS includes data from assays, tests, measurements, and observations. Researchers can enter data directly into the system or integrate LIMS with instruments to automatically capture data, eliminating manual entry errors and ensuring data accuracy.
As standardization of the experiments and processes is fundamental for providing accurate, real-life results, LIMS allows the creation of protocols, specifying step-by-step procedures and instructions for different experiments. Having this functionality, LIMS ensures adherence to protocols, promotes consistency in data collection, and allows for easy replication of experiments.
As the importance of high-throughput technology grows every year in the field of preclinical research, instrument integration is recognized as an integral part of LIMS to facilitate the seamless integration of data generated by various laboratory instruments and equipment. This integration enables the automatic capture and storage of instrument-generated data, reducing the risk of data transcription errors and simplifying data management. Instruments such as spectrophotometers, chromatography systems, and robotics can be connected to LIMS, enabling direct data transfer and reducing the human error connected with this process.
LIMS also plays an important role in Quality Assurance in preclinical research. By incorporating regulatory standards and internal quality control procedures, LIMS facilitates adherence to Good Laboratory Practice (GLP) guidelines, data integrity, and audit trials. LIMS assists in managing documentation related to regulatory compliance, including sample chain of custody, quality control records, and Standard Operating Procedures (SOPs).
As reporting is particularly important to track the quality and progress of preclinical research, LIMS often includes tools for generating customizable reports and performing basic data analysis. Researchers can extract relevant data subsets, create graphical representations, and perform basic statistical analysis within the LIMS environment and integrate with external data analysis tools for more comprehensive analysis.
2.4 Cloud-Based Storage and Computing Solutions for Secure and Scalable Data Management (Willard et al. 2016; Berman 2012; Ngiam and Khor 2015)
The way how preclinical research data is managed and analyzed was revolutionized by cloud-based data storage solutions. Cloud platforms offer robust security measures, including encryption, access controls, and regular backups, ensuring the confidentiality and integrity of research data. Researchers can store large volumes of data in the cloud without the need for expensive on-site infrastructure, reducing on-site infrastructure costs and associated administrative burden. Moreover, these types of storage solutions provide scientists with convenient and universal access to their data from anywhere with an internet connection. This accessibility facilitates seamless collaboration among research teams, allowing multiple users to access, share, and work on the same data simultaneously. It promotes real-time collaboration, streamlines communication, and enhances productivity.
What is important, to maintain data redundancy and backup within preclinical research using the cloud-based solutions, research data is replicated and stored across multiple servers and data centers, ensuring high availability and reducing the risk of data loss due to hardware failures or natural disasters. Automated backup processes provide an additional layer of data protection, enabling easy data recovery in case of accidental deletion or system failures.
Cloud-based storage solutions facilitate data integration and interoperability from various sources, such as laboratory instruments, electronic medical records, or external databases, in a centralized cloud environment. This integration allows for comprehensive analysis and correlation of diverse datasets, leading to more meaningful insights and discoveries.
Moreover, cloud computing provides researchers with the access to powerful computational resources for data analysis and processing. Scientists can leverage cloud-based computing environments to perform complex data analyses, simulations, and modeling without the need for extensive on-site computational infrastructure. Cloud platforms offer high-performance computing capabilities, enabling researchers to process large datasets and perform computationally intensive tasks efficiently.
Cloud computing platforms offer scalability, allowing researchers to scale up or down their storage resources based on their needs. Researchers can easily accommodate growing data volumes without investing in additional hardware or infrastructure. This scalability offers cost-effective solutions and flexibility in managing preclinical research data compared to maintaining on-premises infrastructure. Researchers can avoid repeated upfront investments in hardware, maintenance costs, and software licenses. Cloud services typically operate on a pay-as-you-go model, allowing researchers to pay only for the resources they consume, making it a more cost-effective option for preclinical research data management and analysis.
2.5 Data Analysis and Visualization Tools for Deriving Meaningful Insights from Preclinical Data (Park et al. 2019; Tian and Greenberg 2019; Tomczak and Czerwińska 2018; Sutherland and Rahman 2017; Campillos and Kuhn 2019; Cheng et al. 2019; Wang et al. 2018)
Digitization empowers researchers to employ advanced computational methods, machine learning algorithms, statistical tools, and data visualization techniques for comprehensive analysis and interpretation of preclinical research data. These capabilities enable researchers to uncover valuable insights, identify patterns, discover relationships, and effectively communicate their findings, ultimately advancing scientific knowledge and contributing to developing novel therapeutic approaches.
Digitization enables the application of computational methods and machine learning algorithms to analyze preclinical research data. These advanced techniques can handle large datasets, identify complex patterns, and discover hidden relationships that may not be apparent through traditional data analysis approaches. Moreover, computational methods, such as data mining, clustering, classification, and predictive modeling, allow scientists to uncover valuable insights, make data-driven decisions, and generate hypotheses for further investigation.
Statistical tools are crucial in preclinical research as they enable researchers to analyze data, test hypotheses, evaluate treatment efficacy, and make evidence-based decisions, ensuring robust and reliable scientific findings. These tools help quantify uncertainty, assess significance, and establish the credibility and integrity of research outcomes. Digitization provides access to a wide range of statistical tools and software that facilitate robust analysis of preclinical research data. Having such digital tools, researchers can apply statistical methods, including hypothesis testing, regression analysis, survival analysis, and multivariate analysis, to assess the significance of findings and draw valid conclusions. These tools enable researchers to evaluate the above mentioned steps involved in treatment efficacy, measure variability, determine statistical significance, and assess the impact of various factors on research outcomes.
With digitization, researchers can analyze preclinical research data to identify patterns and discover relationships. By examining data across different variables, time points, or experimental conditions, scientists can uncover correlations, dependencies, and trends that may inform their understanding of disease mechanisms, treatment responses, or biological interactions. This information can guide further experimentation, target selection, or refinement of research hypotheses. By exploring data across different variables, time points, or experimental conditions, researchers can detect recurring trends, associations, and dependencies that may be critical for understanding disease processes and treatment outcomes. These patterns can provide important clues for further investigation and guide the development of targeted interventions.
Moreover, digitization facilitates the exploration of relationships within preclinical research data. By conducting correlation analyses, regression modeling, or other statistical techniques, researchers can uncover connections between different variables or factors. For example, they may identify a correlation between a specific biomarker level and treatment response or observe how certain experimental conditions influence the expression of genes of interest. These relationships help researchers understand the underlying biology and can direct future research efforts.
What is important, the research hypotheses based on the analysis of preclinical data can be refined thanks to digitization. By gaining insights into patterns and relationships, researchers can refine their understanding of disease mechanisms or treatment targets. For example, if data analysis reveals a consistent association between a particular molecular pathway and disease progression, researchers may refine their hypothesis to focus on that pathway for further investigation. This iterative process of hypothesis refinement contributes to the advancement of preclinical research.
As target selection is a critical step in preclinical research and drug development, digitization plays a vital role in this process, supporting the identification and selection of potential therapeutic targets. By analyzing preclinical research data, researchers can identify molecules, pathways, or biological targets that are strongly associated with disease processes or treatment responses. This information helps guide the selection of targets for further investigation or drug development, enhancing the efficiency and effectiveness of preclinical research efforts.
Digitization allows researchers to analyze preclinical research data to identify potential therapeutic targets. By examining various types of data, such as genomic, proteomic, or phenotypic data, researchers can uncover molecules, pathways, or biological targets that are strongly associated with disease processes or treatment responses. For example, through transcriptomic analysis, researchers may identify genes that are differentially expressed in disease states compared to healthy conditions. These differentially expressed genes can serve as potential targets for further investigation.
Digitization enables the integration of diverse datasets from various sources, including public databases, in-house experiments, and literature. By combining and analyzing multiple datasets, researchers can gain a comprehensive view of the molecular landscape associated with a particular disease or biological process. This integration allows for the identification of potential targets that exhibit consistent patterns across different datasets, increasing confidence in their selection.
Computational methods and machine learning algorithms are leveraged by digitization to analyze complex datasets and identify potential therapeutic targets. These techniques enable the exploration of large-scale data, uncovering hidden patterns and associations that may not be apparent through traditional analysis methods. For example, machine learning algorithms can identify molecular features or signatures that are predictive of disease progression or treatment response, guiding the selection of targets with high potential for therapeutic intervention.
Prioritization of potential targets is strongly supported by digitization based on various criteria, such as target druggability, relevance to disease mechanisms, and feasibility of intervention. By integrating data from preclinical studies, clinical trials, and biomedical literature, researchers can assess the evidence supporting the potential therapeutic value of a target. Additionally, digitization facilitates the validation of potential targets through in vitro and in vivo experiments, further confirming their suitability for drug development.
Digitization allows for the analysis of complex target networks and interactions within biological systems. By examining the relationships between potential targets and their associated pathways or networks, researchers can gain insights into the broader biological context and potential cross-talk between different targets. This understanding is crucial for selecting targets that can modulate key disease-related processes or pathways.
By leveraging digitization, researchers can streamline the process of target selection in preclinical research. The analysis of diverse datasets, integration of multiple data sources, and application of computational methods enable the identification and prioritization of potential therapeutic targets with higher precision and efficiency, ultimately enhancing the success rate of drug discovery and development.
3 Enhancing Data Management in Preclinical Research Through Digitization
Efficient data management is crucial in preclinical research to ensure accurate, secure, and accessible data throughout the research process. Digitization offers numerous advantages in data management, streamlining workflows, and improving overall research efficiency. This section focuses on the benefits and strategies of enhancing data management through digitization in preclinical research.
3.1 Importance of Robust Data Management in Preclinical Research
Robust data management in preclinical research is crucial for maintaining data integrity, ensuring reproducibility, complying with regulatory standards, protecting data security, and enabling effective data analysis and decision-making. By implementing rigorous data management practices, researchers can maximize the value of their research data, accelerate scientific discoveries, and contribute to the advancement of knowledge in the field of preclinical research. Preclinical research generates vast amounts of data, ranging from experimental results to imaging data, genomic data, and clinical observations. By implementing standardized data collection protocols, proper documentation, and quality control measures, robust data management minimizes errors, enhances data accuracy, and ensures the reliability of research findings. Moreover, robust data management promotes research reproducibility and transparency. Well-managed and properly documented data allows other researchers to reproduce experiments, validate findings, and build upon existing knowledge. Transparent data management practices, such as data sharing and open science initiatives, facilitate collaboration, promote cross-validation, and enable the scientific community to verify research outcomes.
It is important to remember that preclinical research involves sensitive data, including patient information, animal model data, and experimental results. Robust data management ensures compliance with regulatory standards and ethical guidelines, such as the HIPAA and the General Data Protection Regulation (GDPR). Adhering to these standards protects the privacy and confidentiality of research subjects and prevents potential legal and ethical implications. Effective data management addresses data security concerns and ensures the protection of preclinical research data from unauthorized access, loss, or corruption. Implementing secure data storage, backup systems, and access controls safeguards data integrity.
Preclinical research generates valuable data that may have long-term significance. Robust data management includes appropriate data archiving, version control, and data retention policies. Proper documentation and retention of research data ensure its availability for future reference, meta-analyses, and potential re-evaluation. Preserving research data over time enhances the integrity and legacy of preclinical research. Effective data management facilitates data analysis and data-driven decision-making in preclinical research. Well-organized and accessible data allows researchers to perform comprehensive analysis, apply statistical methods, and derive meaningful insights. This supports evidence-based decision-making, hypothesis generation, and the identification of potential therapeutic targets or lead compounds.
3.2 Role of Digitization in Improving Data Accuracy, Integrity, and Accessibility
Digitization plays a vital role in improving data accuracy, integrity, and accessibility in preclinical research. It enables standardized data collection, enhances data tracking and version control, promotes data sharing and collaboration, and ensures the security and confidentiality of research data. By leveraging digital tools and platforms, researchers can have greater confidence in the quality and reliability of their data.
Digitization minimizes errors and improves data accuracy through automated data capture and standardized data entry. Digital tools also often include data validation checks and built-in quality control measures to ensure the accuracy and completeness of data.
What is particularly important in preclinical studies, digitization ensures data integrity by providing mechanisms for data tracking, version control, and audit trails. With digital platforms and systems, researchers can easily track and document any changes made to the data, maintaining a transparent record of data modifications. This enhances data integrity, allowing researchers to trace the evolution of the data and verify the accuracy of the results.
Moreover, digitization promotes data standardization and adherence to data governance principles. Through digital platforms and data management systems, researchers can define standardized data collection protocols, data dictionaries, and metadata standards. This ensures consistency and uniformity in data collection across studies, making it easier to compare and integrate data from different sources or experiments.
The accessibility of preclinical research data is promoted by digitization through enabling centralized and secure storage, retrieval, and sharing. Cloud-based storage solutions provide researchers with the ability to access data from anywhere, at any time, facilitating collaboration among geographically dispersed teams.
Robust security measures to protect the confidentiality and privacy of preclinical research data are crucial to secure data storage and it is possible through encryption, user access controls, and authentication mechanisms to safeguard data from unauthorized access or breaches.
Reliable data backup and disaster recovery mechanisms are possible thanks to digital platforms and cloud-based storage solutions that automatically backup data, preventing data loss due to hardware failures or unforeseen events. This ensures data continuity and reduces the risk of losing valuable research data.
3.3 Integration of Diverse Data Sources and Data Standardization for Enhanced Collaboration
Digitization promotes data standardization, facilitating enhanced collaboration in preclinical research. By integrating datasets from various sources, researchers can gain a comprehensive understanding of complex biological processes, foster interdisciplinary collaborations, and leverage the expertise of multiple research teams. The integration and standardization of data enhance reproducibility, support cross-domain insights, and enable meta-analyses, ultimately advancing preclinical research and improving the translation of research findings into clinical applications.
Digitization enables the integration of data from various sources, such as genomics, proteomics, imaging, clinical observations, and electronic health records. By consolidating these diverse datasets, researchers gain a comprehensive view of the research landscape, uncovering potential correlations and insights that may not be apparent when analyzing individual datasets in isolation. Integration of diverse data sources promotes a multidimensional understanding of disease mechanisms, treatment responses, and therapeutic targets.
Collaborative Data Analysis is facilitated by digitization by providing a unified platform for researchers to access and analyze integrated datasets. Digital tools and platforms support data sharing, enabling multiple researchers to work simultaneously on shared datasets. This collaborative approach promotes cross-disciplinary collaborations, fosters knowledge exchange, and leverages the expertise of different research teams, enhancing the quality and depth of data analysis and interpretation.
Data standardization and interoperability, promoted by digitization, enable easier integration and analysis of datasets from different research groups or institutions. Standardized data formats, metadata, and data dictionaries ensure consistency in data collection, annotation, and representation across studies. This standardization facilitates data sharing, meta-analyses, and comparison of results, allowing researchers to build upon existing knowledge and accelerate scientific discoveries.
In turn, integrated and standardized data promote research reproducibility. By sharing well-documented and harmonized datasets, researchers can replicate experiments, validate findings, and verify the robustness of research outcomes. The ability to access and analyze integrated datasets enhances transparency, encourages open science practices, and supports the reproducibility of preclinical research studies, reinforcing the reliability and credibility of research findings.
Integration of diverse data sources enables researchers to gain cross-domain insights, fostering interdisciplinary collaborations and expanding the understanding of complex biological processes. For example, combining genomic and imaging data may reveal correlations between genetic variations and phenotypic characteristics. Integrating clinical data with preclinical research findings can help bridge the translational gap, facilitating the identification of potential therapeutic targets and informing clinical trial design.
What is important, digitization facilitates data harmonization for meta-analysis, enabling researchers to pool and analyze large-scale datasets from multiple studies. Meta-analyses conducted on integrated datasets, provide valuable insights into population-level trends, treatment responses, and predictive factors in preclinical research.
3.4 Real-Time Data Availability and Monitoring for Timely Decision-Making
Real-time data availability and monitoring are essential components of digitization in preclinical research, enabling researchers to make quicker decisions. Instant access to preclinical research data eliminates delays associated with manual data collection and processing. Researchers can enter data directly into electronic systems, making it immediately available for analysis and monitoring. This real-time access to data reduces the time lag between data collection and decision-making, enabling researchers to respond promptly to emerging trends, unexpected observations, or critical events.
What is important, digital platforms and data management systems enable continuous monitoring of preclinical research data. Researchers can set up automated alerts and notifications based on predefined criteria, allowing them to stay informed about key metrics, data trends, or experimental milestones. Continuous data monitoring facilitates proactive decision-making by identifying potential issues or deviations in real time, allowing researchers to intervene promptly and adjust experimental protocols, treatment strategies, or study designs as needed.
Moreover, real-time data availability facilitates the early identification of trends, patterns, and emerging insights in preclinical research. By analyzing data as it becomes available, researchers can detect subtle changes or correlations that may indicate the efficacy of a treatment, the progression of a disease, or the need for adjustments in experimental conditions. Early identification of such trends enables researchers to make timely decisions regarding the continuation, modification, or termination of a study, potentially saving time, resources, and effort.
Rapid response to safety and efficacy signals in preclinical research has been made possible thanks to the real-time data availability and monitoring. Adverse events or unexpected treatment responses can be promptly detected, allowing researchers to take immediate actions, such as adjusting dosages, modifying experimental protocols, or initiating additional safety assessments. Timely decision-making based on real-time data helps ensure the welfare of research subjects and improves the overall quality and validity of preclinical research outcomes.
Digitization enables iterative experimentation and optimization of preclinical research. Real-time data availability allows researchers to analyze preliminary results and make informed decisions regarding the next steps in the research process. This iterative approach facilitates the fine-tuning of experimental designs, treatment regimens, or data collection methods based on ongoing data analysis and evaluation. By continuously optimizing research parameters, researchers can enhance the efficiency, accuracy, and success rate of preclinical studies.
4 Digitization and Experimental Design in Preclinical Research
Through leveraging digital tools and technologies, digitization plays a crucial role in improving experimental design in preclinical research. It enables virtual screening, which involves using computational methods to identify potential therapeutic targets and screen large libraries of compounds. Through virtual screening, researchers can prioritize and select target molecules or pathways for further investigation, saving time and resources compared to traditional screening methods.
Moreover, digitization facilitates in silico modeling and simulation, using computer-based algorithms and simulations to predict and evaluate the properties and behaviors of molecules. These methods can be applied to optimize drug candidates, assess pharmacokinetics, simulate protein-drug interactions, and predict the potential efficacy or toxicity of compounds. In silico modeling allows researchers to make informed decisions regarding experimental designs and prioritize compounds for further preclinical evaluation.
The data-driven experimental design can be improved by leveraging existing data to inform future research directions. By analyzing preclinical research data, researchers can identify patterns, correlations, and trends that can guide the design of subsequent experiments. This data-driven approach helps optimize experimental parameters, improve the efficiency of studies, and increase the likelihood of obtaining meaningful results.
Importantly, digitization supports high-throughput screening (HTS) and automation, allowing researchers to rapidly test a large number of compounds or samples, even utilizing robotic platforms and digital interfaces in a high-throughput manner. This automation improves efficiency, minimizes human error, and enables the screening of vast compound libraries or biological samples.
Digital platforms and LIMS streamline experimental workflow management in preclinical research. These systems provide centralized repositories for protocols, sample tracking, and data management. Researchers can efficiently track experimental progress, monitor resource utilization, and ensure adherence to standardized protocols, promoting consistency and reproducibility in experimental design. The optimization of sample size and statistical power in preclinical studies can be made through computational methods and statistical tools, when researchers can perform power calculations and sample size estimations based on desired effect sizes, variability, and statistical significance thresholds. This optimization ensures that experiments are adequately powered to detect meaningful effects and reduces the need for unnecessary animal use or resource allocation (Hagan et al. 2019; Rodgers and Levin 2017; Ruusuvuori et al. 2018; Tanoli et al. 2019).
4.1 Utilizing Digital Tools for Experimental Design and Protocol Optimization (Gao et al. 2020; Morrissey et al. 2016; Rodgers and Levin 2017; Tung and O’Brien 2017; Williamson et al. 2009)
Utilizing digital tools for experimental design and protocol optimization in preclinical research offers several advantages, including improved efficiency, increased accuracy, and data-driven decision-making. Digital tools enable virtual screening and molecular modeling techniques to predict the interactions between molecules and their targets. Researchers can use computational methods to assess the binding affinity, selectivity, and potential off-target effects of compounds. This information aids in the design of experiments and the selection of promising compounds for further evaluation, reducing the time and cost associated with traditional screening methods.
Thanks to digitization, a wide range of statistical analysis software is available to perform power calculations, sample size estimations, and statistical hypothesis testing. By leveraging these tools, researchers can optimize their experimental designs by ensuring sufficient statistical power to detect meaningful effects and reduce the risk of false positives or negatives.
Digital tools, such as Design of Experiments (DOE) software packages, assist researchers in designing efficient and robust experiments. These tools help determine the optimal combination of factors, levels, and interactions to achieve the desired experimental objectives while minimizing variability and resource utilization. By systematically exploring the experimental space, researchers can identify key factors influencing outcomes and optimize experimental conditions.
Data visualization and analysis capabilities enable researchers to explore and interpret complex datasets. Visualization tools allow for the graphical representation of experimental results, facilitating the identification of trends, patterns, and outliers. Data analysis software offers various statistical and computational algorithms to analyze experimental data, extract insights, and derive quantitative measures of effect size or significance.
Moreover, LIMS platforms facilitate experimental design and protocol optimization by providing centralized management of protocols, samples, and data. As described above, researchers can document and share experimental protocols, track sample information, and record experimental outcomes within the LIMS. This centralized system enhances collaboration, standardization, and reproducibility, ensuring consistency in experimental procedures across research teams.
Digital tools enable iterative design and feedback loops, allowing researchers to refine experimental protocols based on initial results or pilot studies. By continuously analyzing and integrating data generated during the course of the research, researchers can adapt their experimental designs to optimize parameters, adjust treatment regimens, or modify sample selection criteria. This iterative approach increases the efficiency and success rate of preclinical experiments.
4.2 Digital Platforms for Sample Tracking, Experimental Workflows, and Protocol Management (Danziger et al. 2018; Li et al. 2019; Ratner 2010; Tarca et al. 2016; Zhang et al. 2019)
Digital platforms enable researchers to track samples throughout the preclinical research process. These platforms provide functionalities for assigning unique identifiers to samples, recording sample information (such as collection date, storage conditions, and experimental group), and tracking sample movement and usage. With barcode or Radio Frequency Identification (RFID) technologies, researchers can easily scan and update sample information, ensuring accurate sample tracking and minimizing human errors. Digital platforms also support inventory management, allowing researchers to monitor stock levels, expiration dates, and reordering of supplies.
Moreover, digital platforms facilitate the management of experimental workflows, ensuring efficient and standardized processes. Researchers can create digital protocols that outline the step-by-step procedures, equipment requirements, and data collection points for each experiment. These protocols can be accessed, followed, and documented digitally, providing consistency and reproducibility across experiments and research teams. Digital platforms also allow for the integration of data capture instruments, such as automated analyzers or imaging systems, streamlining data acquisition and minimizing manual data entry.
Thanks to digital platforms, it is possible to maintain centralized repositories for storing, sharing, and version-controlling experimental protocols. Researchers can access up-to-date protocols, ensuring consistent implementation of procedures across studies and reducing the risk of protocol deviations. These platforms support collaboration by allowing multiple researchers to work on protocols simultaneously, enabling real-time updates and annotations. Digital protocol management ensures accessibility, traceability, and proper documentation, enhancing transparency and facilitating the replication of experiments.
The integration of data generated from various sources and instruments is supported by digital platforms. Researchers can import and link data files, including images, raw data, and metadata, to specific experiments or samples within the platform. This integration simplifies data retrieval, enables cross-referencing, and facilitates downstream analysis. Some digital platforms also offer built-in data analysis tools or integration capabilities with third-party analysis software, allowing researchers to perform data processing, visualization, and statistical analysis within the platform itself. Examples of digital platforms for sample tracking, experimental workflow management, and protocol management include Electronic Laboratory Notebooks (ELNs), LIMS, and cloud-based research management platforms.
4.3 Use of Virtual Screening and in Silico Methods for Target Identification and Compound Selection (Ertl et al. 2000; Friesner et al. 2004; Schneider et al. 1999; Shoichet 2004; Wang et al. 2005; Willett et al. 1998)
Virtual screening and in silico methods have become valuable tools in preclinical research for target identification and compound selection. These computational approaches offer efficient and cost-effective ways to prioritize and evaluate potential drug candidates. Virtual screening involves the computational screening of large chemical databases or libraries to identify molecules that have a high probability of binding to a specific target of interest. This process typically utilizes molecular docking algorithms to predict the binding affinity and pose of small molecules against the target’s three-dimensional structure. By virtually screening millions of compounds, researchers can prioritize a subset of molecules for further experimental evaluation.
Both ligand-based and structure-based approaches are employed within virtual screening methods. Ligand-based methods involve comparing the chemical features and properties of known active compounds against the target to identify structurally similar molecules. Structure-based methods rely on the target’s three-dimensional structure to predict the binding affinity and interactions of small molecules. These approaches provide complementary strategies for target identification and compound selection.
Pharmacophore modeling is a technique used in virtual screening to identify the essential structural features required for a molecule to interact with a target. By analyzing the active compounds and their common chemical features, a pharmacophore model is generated, representing the key interactions necessary for activity. This model can then be used to screen compound libraries and identify molecules that match the pharmacophore, aiding in target identification and compound selection.
Virtual screening and in silico methods also include Quantitative Structure-Activity Relationship (QSAR) Analysis involves the development of computational models that correlate the chemical structures of compounds with their biological activities. By training QSAR models on known activity data, researchers can predict the activity of new compounds against a target. These predictions guide compound selection by identifying molecules with a higher likelihood of desired activity, reducing the need for extensive experimental testing.
To evaluate the pharmacokinetic and toxicological properties of compounds, the Absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction models are used. By using computational algorithms, researchers can predict parameters such as solubility, permeability, metabolic stability, and potential toxicities. ADMET prediction aids in compound selection by identifying candidates with favorable drug-like properties and reducing the risk of failures in later stages of drug development.
In silico methods can also be employed to identify potential new uses for existing drugs. By analyzing databases of known drugs and their interactions, researchers can identify off-target effects and explore new therapeutic indications. This approach of drug repurposing offers a cost-effective strategy for identifying candidates with established safety profiles and known pharmacokinetics.
Virtual screening and in silico methods significantly accelerate the early stages of drug discovery by reducing the number of compounds for experimental testing, optimizing the selection of lead candidates, and providing insights into the structure-activity relationships. However, it is important to note that these computational approaches are hypothesis-generating tools and still require experimental validation.
4.4 Application of Machine Learning Algorithms for Predictive Modeling and Simulation Studies (Cherkasov et al. 2014; Cramer et al. 2020; Ekins et al. 2007; Sliwoski et al. 2014; Wallach et al. 2015)
Machine learning algorithms are increasingly being applied in preclinical research for predictive modeling and simulation studies. These algorithms have the ability to analyze complex datasets, identify patterns, and make predictions or classifications. Predictive models that estimate various outcomes in preclinical research can be built with the use of machine learning algorithms. For example, these algorithms can predict drug efficacy, toxicity, pharmacokinetic properties, or biological activity based on input features such as chemical descriptors, genomic data, or experimental results. By training the algorithms on known data, they can learn patterns and relationships to make accurate predictions on new or unseen data.
Machine learning algorithms play a significant role in the Structure-Activity Relationship (SAR) Analysis, which is an essential part of drug discovery. These algorithms can learn from chemical structures and their corresponding activities to identify key features associated with a compound’s activity against a particular target. SAR models built using machine learning can aid in compound selection, design, and optimization by predicting the activity of new compounds based on their structural similarity to known active compounds.
The process of toxicity prediction can be also supported by machine learning algorithms. By training on data that contains information about chemical structures and corresponding toxicological outcomes, these algorithms can learn to classify compounds into toxic and non-toxic categories. Toxicity prediction models can help prioritize compounds for further experimental evaluation, thus reducing the cost and time associated with toxicity testing. Moreover, machine learning algorithms can be employed in predicting potential interactions between drugs. By analyzing the chemical structures, pharmacokinetic properties, and known drug-drug interaction data, these algorithms can identify pairs of drugs that are likely to interact. This information aids in predicting potential adverse drug reactions and guiding the selection of drug combinations for further investigation.
Simulation studies also open novel opportunities to use machine learning algorithms for the prediction of the behavior of complex biological systems. The assistance of machine learning algorithms in integrating and analyzing diverse datasets from multiple sources is becoming recognized. These algorithms can automatically extract relevant features from large datasets and identify the most informative variables for predictive modeling. Feature selection techniques enable researchers to focus on the most influential factors and reduce the dimensionality of data, thereby improving the efficiency and accuracy of predictive models.
It is important to note that the successful application of machine learning algorithms in preclinical research requires high-quality and well-curated datasets, appropriate feature engineering, validation, and interpretation of results.
5 Implications of Digitization on Preclinical Research Outcomes
The implications of digitization on preclinical research outcomes are significant and wide-ranging. It is widely recognized that digitalization streamlines various aspects of preclinical research, including data collection, analysis, and collaboration. Automated data capture, electronic data storage, and real-time data availability reduce manual effort, minimize errors, and improve overall efficiency. This leads to faster experimentation, data processing, and decision-making, ultimately accelerating the pace of preclinical research (Arrowsmith 2013; Casadevall and Fang 2016; Leek and Peng 2015; Lynch 2008; Taylor et al. 2015).
Digitization is an important milestone in improving data accuracy, integrity, and traceability. Electronic data capture systems and standardized data entry protocols ensure consistent and error-free data recording. Digital platforms enable data validation, audit trails, and version control, enhancing data quality and reproducibility. This ensures that research outcomes are based on reliable and trustworthy data. Advanced data analysis techniques, including machine learning, statistical modeling, and data visualization have been made possible thanks to digitalization. By harnessing these computational methods, researchers can derive deeper insights, uncover patterns, and make data-driven decisions. Advanced data analysis facilitates the identification of novel relationships, biomarkers, or therapeutic targets, enhancing the overall outcomes of preclinical research.
As previously stated, digital platforms enable seamless integration of data from diverse sources, such as genomics, proteomics, and imaging. This integration facilitates multidisciplinary collaboration, as researchers can easily share, access, and analyze integrated datasets. Collaborative data analysis promotes cross-validation, knowledge exchange, and interdisciplinary discoveries, leading to more comprehensive and impactful research outcomes. Moreover, digitization promotes reproducibility and transparency in preclinical research. Electronic protocols, SOPs, and version-controlled documentation ensure consistent experimental procedures and facilitate protocol sharing. Digital platforms also allow for the sharing of raw data, analysis code, and research findings, promoting transparency and reproducibility of research outcomes.
As cost reduction and resource optimization play an important role in sustainability of preclinical research, digitization through electronic data storage reduces the need for physical storage space and decreases administrative overhead. Computational modeling and simulation techniques can reduce the number of experiments required, saving time, resources, and animal models. Digitalization also enables virtual collaborations and remote access to data, reducing travel and infrastructure costs.
6 Conclusion
The introduction of digital technologies has influenced the entire continuum of preclinical research activities. This chapter describes the many different aspects of preclinical research and how it is affected by digitization. These include the digital tools and technologies in preclinical research, the ways in which digitization enhances data management in preclinical research, as well as experimental design in preclinical research. These aspects are universal and applicable both in high-income countries and low-and middle-income countries, though the relevant rate of implementation may differ, depending on the local context of implementation.
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Michalska-Falkowska, A., Sargsyan, K. (2024). Digitalization in Preclinical Research: Advancements and Implications. In: Kozlakidis, Z., Muradyan, A., Sargsyan, K. (eds) Digitalization of Medicine in Low- and Middle-Income Countries. Sustainable Development Goals Series. Springer, Cham. https://doi.org/10.1007/978-3-031-62332-5_11
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