NeoTrident, along with its parent company, Suzhou NeoTrident Software Co., Ltd., passed the ISO/IEC 27001:2013 certification from the British Standards Institution (BSI). This milestone marks NeoTrident’s internationally recognized capability in information security management, strengthening its position as a secure and reliable IT provider in life and material sciences.
This certification reinforces NeoTrident’s commitment to safeguarding customer data and system security, further enhancing its product offerings like the iLabPower Collaborative Innovation Cloud. The certification affirms that NeoTrident adheres to rigorous international standards in areas such as data encryption, asset management, and system security. The company continues to prioritize security management, fostering customer trust and ensuring robust data protection across its digital platforms.
With over 1,000 R&D institutions using NeoTrident’s solutions, the ISO27001 certification adds credibility to their extensive service portfolio in fields ranging from research to production.
With the rise of electronic lab notebooks, molecular biologists and chemists are now able to collaborate more efficiently, streamlining the development of new drugs through standardized workflows. However, as the life sciences industry continues to evolve, there are several challenges that researchers face when working with increasingly complex data and processes in bio-new drug development. One of the main challenges is the need for specialized software solutions that can handle the growing demands of sequence analysis, data management, and experimental reproducibility.
Data Complexity As drug development processes become more sophisticated, the amount of data generated from biological experiments has increased significantly. Managing and analyzing this data efficiently is crucial for timely and accurate results. Complex workflows, such as gene editing, protein synthesis, and antibody development, require software tools that can handle large datasets, perform detailed analyses, and ensure data traceability across multiple experiments and teams.
R&D Costs and Efficiency Developing new drugs requires substantial investment in research and development. For laboratories and pharmaceutical companies, optimizing the efficiency of R&D processes is critical to reducing costs and shortening development timelines. Time-consuming tasks, such as manually curating sequences, calculating biochemical properties, and designing primers, can slow down research and lead to errors. Streamlining these processes with integrated tools is essential to improve productivity and ensure high-quality outcomes.
Some researchers have tried using a variety of standalone tools to manage data, but this approach often leads to fragmented workflows, data silos, and inconsistent results, ultimately wasting valuable time and resources.
To address these challenges, NeoTrident offers a solution: the iLabPower SEQ Sequence Editor. This tool, part of the iLabPower R&D management platform, provides researchers with an integrated and efficient platform for biological sequence design and analysis. Equipped with visual sequence editing functionality, the tool also allows researchers to document the design process seamlessly in the electronic lab notebook, ensuring full traceability and reproducibility.
iLabPower SEQ Sequence Editor Overview The iLabPower SEQ Sequence Editor enables users to manage the entire sequence design process—from sequence creation and editing to validation—all within a single platform. For example, in plasmid construction, a common molecular biology experiment, essential steps such as gene and vector selection, PCR primer design, pre-assembly cutting with appropriate endonucleases, and verification via sequencing results can all be conducted within the Sequence Editor. By consolidating these workflows, the platform ensures traceability and reproducibility, which are essential for reliable research. Moreover, applications like CRISPR and circular RNA construction follow similar workflows, making iLabPower SEQ Sequence Editor versatile for a range of bio-design processes.
Key Features of iLabPower SEQ Sequence Editor
Sequence Creation: The editor supports various ways to create sequences:
Import FASTA, GenBank, and other annotated sequence files.
Customize sequence labeling for efficient management and searching.
Automatically process sequences, including case conversion, coordinate generation, and sequence editing operations, saving time and reducing manual errors.
Visual Sequence Display: The editor allows users to visualize sequences as linear or circular maps, displaying annotations, primers, and restriction sites in a personalized format. This makes it particularly useful for designing structures such as plasmids, CRISPR, and miRNA.
Nucleic Acid Sequence Analysis:
DNA Property Calculation and Primer Design: Automatically calculates properties like annealing temperature, GC content, and sequence length for primer design. The built-in Primer3 tool generates primer pairs based on user-specified parameters.
Enzyme Site Display: Identifies restriction enzyme sites using the NEB-Enzyme Finder and displays them for faster sequence editing.
Open Reading Frame (ORF) Recognition: The editor automatically identifies ORFs, supporting gene expression analysis, which is crucial for drug development.
Amino Acid Sequence Analysis:
Protein Biochemical Characterization: Automatically translates DNA sequences into amino acids, enabling analysis of protein properties, including hydrophobic regions, isoelectric points, and molecular weight. It also facilitates antibody design by identifying CDR (Complementarity-Determining Region) regions critical for antigen recognition.
Comparative Analysis:
BLAST: Provides one-click submission of sequence fragments to NCBI’s BLAST tool for functional prediction.
Dual Sequence Alignment: Supports alignment of DNA or amino acid sequences, helping users visualize conserved regions and sequence variations.
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iLabPower SEQ Sequence Editor in Bio-New Drug Development
New biopharmaceuticals have shown tremendous promise in treating diseases once deemed incurable, such as cancer and genetic disorders. With its intuitive, efficient, and secure design, iLabPower SEQ Sequence Editor offers a new approach to innovative research in laboratories and pharmaceutical companies. From upstream design to downstream validation, all sequence-related tasks can be conducted on a single platform with robust data security and reproducibility.
Moreover, NeoTrident is committed to continually upgrading the software, improving user interaction, and expanding its bioinformatics capabilities to better serve the bio-new drug R&D landscape. By addressing the unique needs of researchers, NeoTrident aims to support intellectual property protection and accelerate the pace of innovation in biopharmaceuticals.
In recent years, the application of Artificial Intelligence (AI) in drug discovery and development (D&D) has become increasingly prominent. AI has significantly improved the efficiency of drug research by predicting drug efficacy and toxicity, automating the design of drug molecules, and accelerating clinical trials. Faced with the impact and opportunities brought by AI, pharmaceutical companies usually take one of three approaches: developing AI capabilities internally, purchasing mature and specialized AI platforms, or outsourcing AI-driven drug discovery and development (AIDD) to third-party providers. But which strategy is the best?
Is Fully Outsourcing Your AIDD a Safe Bet?
Many companies opt to outsource their AIDD efforts, as it seems to save time and reduce costs. However, this approach presents some significant challenges.
First, data security is a major concern. Data is the lifeblood of drug discovery, and a leak could cause severe damage to a company’s business. Once data is outsourced, it becomes difficult to ensure its complete protection. Secondly, data ownership is equally crucial. Outsourcing may result in a company losing ownership of vital data, which could weaken its competitive edge. Furthermore, the quality of services provided by outsourcing companies can vary widely. If an unreliable partner is chosen, it may slow down R&D progress or lead to inaccurate research results.
Therefore, outsourcing AIDD could lead to higher long-term costs. On one hand, outsourcing expenses may increase over time as data grows; on the other hand, the financial losses due to security or service issues might exceed initial savings.
Why Emphasize Autonomous Mastery of AI Tools and Data?
While achieving autonomous control over AI tools and data may require a larger investment in the short term, it offers considerable long-term benefits.
First, professional drug researchers are more likely to achieve better results using AI tools than external partners who may lack a deep understanding of drug research. Internal teams can comprehend research objectives more thoroughly and use AI more effectively. Additionally, owning AI tools and data ensures that a company’s intellectual property and data assets are protected, securing data ownership and preventing security breaches. Furthermore, autonomous control provides greater flexibility. Companies can adapt AI tools to their specific needs and quickly adjust to changes in R&D strategies without waiting for an outsourcing partner’s response. This approach also helps build and retain AI expertise within the company. As AI becomes a core tool in drug development, companies that possess in-house expertise will have a clear advantage. Finally, controlling AI tools and data helps ensure consistency and quality, avoiding errors related to data transfers and conversions.
In the long run, owning AI tools and data can also be more cost-effective. Once a company establishes its own AI platform, it only needs to cover maintenance costs instead of recurring, often escalating, outsourcing fees.
Does Autonomous Mastery Mean Developing AI Platforms Internally?
Autonomous mastery of AI tools doesn’t necessarily require developing them from scratch. In fact, purchasing a mature, low-barrier AI platform from a specialized provider is often a more efficient and cost-effective solution.
To facilitate the integration of “AI+Pharmaceuticals” and empower drug R&D companies to easily and independently manage AI tools and data, Trunetech offers an advanced, user-friendly AI platform called MaXFlow.
MaXFlow, developed by Neotrident, is a next-generation molecular simulation and AI innovation platform aimed at all frontline experimental scientists, computational simulation experts, and AI specialists. It covers a wide range of R&D fields, including innovation discovery and process development. MaXFlow enables users to perform drug design, predict drug properties, manage research data, and much more. Its ease of use ensures that even scientists with no prior experience in molecular simulation or AI can quickly get started.
Easily build 3D models of molecules, proteins, nucleic acids, etc.
Enhance modeling efficiency through component and workflow technology
Connect with the SDH scientific data genome platform to improve data acquisition
Open environment that integrates and encapsulates various algorithms for full algorithmic freedom
SaaS cloud model that seamlessly connects with background supercomputing resources to ensure computational power availability
Extensive APP resources for practical application scenarios, allowing molecular simulation and AI technologies to be accessible with zero barriers
Sharing of physical/AI models to capture expert experience and knowledge
A platform-level solution that provides robust file management, user role, and permission management features to ensure data security, internal knowledge transfer, and the accumulation of valuable data assets.
Conclusion
Overall, taking ownership of AI tools and data is a wise and strategic choice for drug development companies in the long term. This approach not only safeguards a company’s data assets and intellectual property, enhances flexibility, and nurtures in-house talent, but also reduces costs over time. Partnering with a professional AI platform provider is an effective way to achieve this goal. High-quality AI platforms are not only mature and easy to use but also provide strong support, enabling drug development companies to better leverage AI technology.
In the “AI+Pharmaceuticals” field, both IT professionals and pharmaceutical researchers face challenges. Regardless of the chosen strategy, clear communication and collaboration between these teams are critical. Every drug development company should carefully evaluate its AI strategy, consider whether there are better options, and determine how to use AI more efficiently to unlock greater value.
For large molecule drug analysis laboratories that still rely on traditional paper-based management systems, several common issues and pain points often arise, including the following:
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Disorganized Management of Test Request Forms
Test request forms are often managed by batch, meaning each analyst may handle multiple forms, taking up significant space and increasing the risk of loss or sample mismanagement. Additionally, since large molecule drug testing involves numerous test items, analysts may struggle to quickly identify the specific test items and samples assigned to them within each batch of requests.
Difficulty in Locating Samples and References
Large molecule drug samples and reference materials are typically stored in small, frozen packages. Managing these with paper ledgers results in large volumes of records, making it challenging for laboratory personnel to quickly locate the necessary items. Furthermore, due to strict temperature management requirements for ultra-low temperature freezers, it’s essential to minimize the number and duration of freezer openings. Therefore, personnel must quickly identify the correct storage boxes for samples and references during access.
Results Reporting and COA Issuance
After analysts complete their experiments and reviews, they still need to locate the paper report forms corresponding to the relevant batches and manually enter the test results, which is time-consuming and prone to delays. The sample management team must then consolidate the results to issue a Certificate of Analysis (COA), adding further workload.
Archiving and Access to Experimental Records
For completed experiments, offline records are typically managed using coded controlled paper. Once the results are reported, the experimenter must register the return of the controlled paper used. As test records accumulate, storage space demands increase, raising archive management costs. Moreover, when Out of Specification (OOS), Out of Trend (OOT), or Analytical Data (AD) anomalies occur, accessing historical records becomes more difficult and time-consuming as the number of records grows.
iLabPower’s Paperless Solution for Large Molecule Laboratories
To address these management issues and pain points, the iLabPower R&D Innovative Digital Platform, developed by NeoTrident, offers a comprehensive paperless solution for analytical laboratory management. This includes the management of sample delivery processes, an electronic laboratory notebook (ELN), and a biological sample management system, all designed to enhance the efficiency of sample management, testing, and analysis in large molecule biopharmaceutical labs.
iLabPower SIP manages the entire workflow, from setting quality standards to distributing samples, assigning test items, and issuing COA reports. It supports the automatic determination of test conclusions and COA generation, helping users streamline testing task assignments and COA organization, ultimately improving the operational efficiency of the analytical lab. The SIP system integrates seamlessly with the ELN, linking each testing task to its corresponding experimental records. Results from the ELN can be directly accessed in SIP, ensuring data authenticity, traceability, and timeliness.
Electronic Laboratory Notebook System
iLabPower ELN
The iLabPower ELN works seamlessly with the SIP system, syncing assigned testing tasks to the ELN. Analysts complete their experiments and record reviews in the ELN, and results are automatically sent back to the SIP, improving the efficiency of results reporting. The ELN includes functions such as instrument data analysis, customizable experiment templates, mobile app-based photo recording, and voice recognition, further improving the organization of experimental records. Additionally, the ELN supports online archiving and full-text search capabilities, reducing document storage costs and facilitating easy access to historical records.
Biological Sample Management System
iLabPower BIMS (Biological Information Management System)
iLabPower BIMS is a specialized product for managing biological samples, such as proteins, cells, plasmids, and strains. It provides users with a comprehensive solution for the management of biological samples in the lab. The system includes a visual interface for commonly used cryopreservation equipment (e.g., refrigerators, liquid nitrogen tanks, baskets, freezing racks, freezing boxes) and features sample retrieval and query functions. This streamlines the process of locating cryopreserved samples and reference materials, reducing the number of times refrigerators or liquid nitrogen tanks need to be accessed, which helps maintain sample stability by avoiding temperature fluctuations.
In a SaaS deployment model, software providers host the software on cloud service providers’ servers. Users purchase appropriate licenses through the SaaS provider to access the software and its associated services. There is no need for users to install, deploy, or upgrade the software on their own systems; they simply log in to enjoy the convenience of its functionality.
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Since services and data are hosted in the cloud rather than on local servers, users may worry about whether their data is adequately protected. In this article, we discuss the key security concerns associated with SaaS and explain how to assess the security capabilities of SaaS providers when selecting a SaaS-based ELN, focusing on aspects like basic security, environmental security, application security, data security, compliance, and the division of security responsibilities.
Public vs. Private Cloud Deployment
Public cloud platforms generally offer greater availability, security, and scalability compared to private cloud platforms. It’s recommended to prioritize well-established public cloud platforms such as AWS, Ali Cloud, or Microsoft Azure, etc.
Security Management Qualifications of Third-Party Providers
When selecting a SaaS platform, it’s essential to assess the basic security capabilities, protection measures, and qualifications of the third-party SaaS provider.
Take AWS as an example. AWS is a globally recognized cloud platform that holds top security certifications, including ISO27001. AWS also adheres to strict data protection standards such as GDPR compliance, and provides services that meet numerous regulatory requirements globally. AWS security measures include physical and environmental security, baseline security, disaster recovery, and business continuity. AWS is widely regarded as one of the most secure and trustworthy cloud platforms on the market.
All business platforms of iLabPower Innovation Cloud Community are deployed on the AWS IAAS platform, utilizing AWS services such as computing, networking, and storage. The iLabPower Innovation Cloud Community relies on AWS’s robust security solutions, including AWS’s cloud firewall, to ensure top-level network security, comparable to the security measures used by AWS itself.
Security Measures for SaaS Applications
The security of the SaaS platform does not automatically extend to the SaaS applications. The applications themselves must have robust security measures in place. For example, in the iLabPower Innovation Cloud Community, SaaS products implement security at various levels: network, database, environment, personnel, and operating systems. Key measures include:
(1) User Access
Access Interface Security
Secure https protocol (SSL encryption) is employed.
URL encryption via algorithms.
Privilege Security
Login passwords are encrypted, and each account is linked to a specific device.
Role-based authorization limits access based on employees’ business system roles.
Access Control
Unique accounts for employees, preventing login by departed staff.
Error protection mechanisms for login credentials.
Audit logs and access records are backed up for auditing purposes.
(2) Data Security
Ensuring read-write separation for data integrity.
Utilizing a high-availability system with hot standby on two servers.
Creating distributed databases to avoid resource bottlenecks.
Implementing lightweight distributed file systems for mass storage and load balancing.
Regular system inspections, data backups, and anti-tampering measures.
Organizational and Personnel Security
Organizational Security: The Innovation Cloud Community team consists of dedicated security teams across design, R&D, and maintenance.
Personnel Security: Employees adhere to relevant laws and policies, and have the knowledge and expertise to perform their duties securely.
Delivery Security: Security is ensured throughout the product lifecycle, from design to deployment and maintenance.
R&D and Maintenance Security: The R&D and maintenance teams work collaboratively on the platform’s architecture, business logic, and ongoing security improvements.
Disaster Recovery and Business Continuity: The system includes disaster recovery plans to minimize service interruptions and data loss.
Regular Penetration Testing
SaaS platforms must regularly conduct penetration tests and provide security reports from accredited security vendors.
Data Privacy and Regulatory Compliance
All data on a SaaS platform, including administrative access logs, should be regularly audited. Compliance assessments help ensure adherence to regulations and appropriate security protocols.
The ownership of user data on a SaaS platform remains with the user. SaaS providers cannot use or sell the data without user consent, and are responsible for securely destroying historical data when no longer needed. SaaS providers also bear responsibility for compensating users in case of data breaches or losses.
iLabPower Innovation Cloud Community’s Privacy Policy clearly outlines how user data is collected, used, stored, and protected, offering transparency and control to users over their data.
Multi-Tenant Data Segregation
SaaS is based on a multi-tenant architecture, where data from different users may be stored in the same environment. Providers must ensure strict data isolation, preventing one user from accessing another’s data. In the case of AWS, network isolation is implemented for each deployment, and measures like trusted packet filters, rate limiting, and anti-spoofing protect tenant data.
For example, communication between virtual machines (VMs) is always routed through trusted packet filters. VMs cannot capture network traffic that is not destined for them. AWS further ensures that VMs within a virtual private network have isolated address spaces that cannot be accessed by external VMs unless specifically configured.
Division of Security Responsibilities
In the SaaS model, security compliance responsibilities are shared between the SaaS provider and the customer. For instance, if a security issue arises due to a vulnerability in the application system, the responsibility lies with the provider. However, if the issue results from weak passwords or identity theft by the user, the responsibility falls on the tenant.
Man-hour management plays a crucial role in the pharmaceutical R&D project management process. For example, in CRO (Contract Research Organizations) services, charges are typically calculated based on the number of hours spent by project personnel multiplied by an hourly rate. Effective man-hour management directly impacts project costs. Additionally, accurate and scientific management of man-hours helps determine if resources are allocated efficiently, providing the necessary insights to optimize resource distribution, ultimately leading to cost reductions and increased efficiency.
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CRO enterprises manage multiple R&D projects simultaneously, often introducing new projects along the way. The data formats and standards of these projects can vary significantly. Traditional project management software, due to its limited flexibility, struggles to handle the complex task of man-hour statistics. As a result, many companies are forced to regularly export man-hour data and manually clean and organize numerous data sets—a labor-intensive process. Project managers often find themselves overwhelmed by the workload, leading to delayed data updates and compromised decision-making support. Furthermore, manual processing increases the risk of data tampering, making it difficult to trace changes and complicating data verification for regulatory purposes.
The SDH Scientific Data Genome Platform provides an efficient, streamlined, and secure solution to these labor management challenges.
▶ Automatic Data Cleaning, Conversion, and Seamless Integration of New Data Tables
On the SDH platform, users can begin by selecting “File Connection & Management” under the Data Connection section and upload the monthly man-hour statistics to SDH.
Upon accessing the data editing page, a range of data cleaning and conversion tools, such as “Delete Empty Rows” and “Inverse Pivot,” can be employed to clean and organize the man-hour data. Additionally, the platform automatically generates “conversion rules” during the cleaning process, which can be saved for future one-click cleaning of similar data tables.
By customizing and saving the cleaned data, users can merge man-hour statistics from multiple months into a single dataset, enabling the quick splicing of multi-month data. Future project hour tables can also be seamlessly integrated with a single step.
▶ Multi-dimensional Reports with One-click Data Synchronization and Updates
Once data cleaning is complete, the platform’s BI reporting function enables comprehensive data analysis. The generated statistical reports can be saved, and subsequent updates to the man-hour data are reflected with a single click, allowing users to quickly access the latest analysis for informed decision-making.
Additionally, the SDH platform can integrate data from other systems, such as project management and material (reagent) management, enabling a comprehensive view of project costs, budgets, and expenditures.
▶ Robust Audit Trails and Permission Management for Data Security and Traceability
The SDH platform ensures that no data modifications can be made, and it includes strict audit trail and permission management features. Every operation is transparent, ensuring data security and traceability, providing multiple layers of protection.
