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The training function in a pharmaceutical facility is vital to site operations. The objective of training is straightforward; training is responsible for employee learning. Site training programs must be effective to accomplish employee learning. Effective training is difficult; the broad scope of topics, trainer communication skills, trainee motivation, logistics, documentation, maintenance of training quality, and other considerations may challenge effective training. Training is a cost; training in some organizations may be restricted by budget, time, facilities, conflicting priorities and criticalities, or other limitations. Identifying problematic aspects in training and appropriately addressing these considerations is crucial to accomplishing effective employee learning. 

Submitted: Aug. 4, 2024
Accepted: Aug. 29, 2024

This manuscript discusses the application of quality risk management (QRM) concepts within a site training program. QRM application helps to identify potential problematic aspects in training; these potential issues may then be addressed by following the QRM process. ICH Q9(R1) is the fundamental document describing QRM (1). Hazard identification (i.e., potential problem identification) is the first step in the QRM process. This paper focuses on problem identification within a comprehensive training process. Specific application of QRM to training and education is identified in ICH Q9(R1).

QRM application in training provides a systematic approach that complements traditional training—a thought process distinct from usual training. Typical training involves information, actions, and procedures (i.e., how to do things); training should also address understanding (i.e., why are things done). QRM identifies potential problems during training—QRM addresses what might go wrong. QRM thus improves training and makes training content more relevant. QRM application facilitates poka-yoke, mistake-proofing, defensive design, or other approaches intended to prevent problems—QRM proactively facilitates problem prevention. Adult learners in pharma are practical in orientation; QRM problem identification is harmonious with adult learning (2). Examples of potential training problems are presented throughout the following discussion:

 Training functions within pharma may be organized in different ways. Training may be the responsibility of each technical function (e.g., engineers train other engineers, chemists train other chemists, etc., within function departments); each function accomplishes their individual personnel training. Centralized training with varying levels of activities is also possible; some centralized functions may be responsible for minimal administrative activities (e.g., personnel training records); others may manage training requests, training content documents, schedule sessions, and periodically monitor training quality—a much more comprehensive responsibility. Some training organizations may be linked to corporate human resource (HR) systems to integrate government-mandated training within employee files. The following discussion addressing QRM in training utilizes a centralized function with responsibility for individualized training and administrative activities. The point of this discussion is the identification of potential problems adjunctive to typical training regardless of organizational structure.

 The role of training management is critical to training program success. Training management is responsible for actual training as well as training function management. Quality systems guidance documents (3–5) identify expectations of management; QRM considerations are also mentioned in these documents. Training management may need to mediate conflicting training time/costs vs. site criticalities with other site functions. Other management training responsibilities are also crucial for success. Site training programs often rely on personnel in other departments to accomplish training objectives; training management must ensure that these individuals know and understand their responsibilities supporting training. Training management also provides an unspoken message to trainees during training; conscientious effort in training with thorough and well-designed training content conveys a message of quality and compliance to employees. A common thread in deficient training is a lack of visible management involvement; one cannot demonstrate commitment by staying in the office and going to meetings. Training management must be “on the floor” when and where training happens (i.e., management by walking around [MBWA]) (6). Current workplace trends in Zoom management exacerbate management lack of visibility. An ongoing commitment to training excellence demonstrated by management visibility helps to develop a quality culture in the organization.

 This discussion is structured according to the FDA lifecycle approach to process validation (7). The process validation model provides a comprehensive approach to training. Process validation is designed, executed, and continually maintained; training activities can likewise follow this same approach. 

 describes three stages in a training lifecycle akin to three stages in process validation. Training stage 1 describes the design of the training topic including content, presentation, and associated elements; stage 2 comprises actual training performance by trainers and trainees; stage 3 addresses the ongoing maintenance of training (i.e., effectiveness, content accuracy, and compliant performance). A basic tenet of process validation is the identification and control of variation; training problems may include performance variation by trainers, uncontrolled logistics, and other causes. Training management should develop a training master plan akin to a validation master plan containing training strategies, approaches, procedures, documentation, and commitments. The following continues previous discussions correlating training with process validation including validation application overview, training content development, trainer qualification, trainee prerequisites, the training process, performance monitoring, management overview, and documentation (8–15). 

QRM terminology may often confuse readers based on common language within their pharma settings—specifically the words ‘hazard’ and ‘risk’. Hazard and risk are often assumed to describe dangerous workplace situations (e.g., fall, slipping, electric shock, overhead machinery, others). Per ICH Q9(R1), ‘hazard’ and ‘risk’ are not synonymous (16,17). Hazard is a source of harm; after a hazard is identified, its risk of causing harm (high, medium, low, or descriptive numeric) is assessed using risk assessment tools or subjective methods. Risk assessment describes the probability of harm occurrence and the level of harm severity; some common risk analysis methods include failure mode and effect analysis (FMEA), preliminary hazard assessment (PHA), and fault tree analysis (FTA); these tools are identified in ICH Q9(R1) Annex I. FMEA integrates frequency, severity, and detection in its Risk Priority Number (RPN), which quantifies risk. 

 more completely defines terminology as explained in Q9(R1) training modules and used in the following discussion. Q9(R1) changed the initial step in the risk assessment process to ‘hazard identification’ from ‘risk identification’. Although this change enabled consistency with the definition of hazard in other regulatory documents, it may cause confusion in common word usage. Hazard identification in training is the primary objective of the following discussion. 

 Quality risk management terminology. Hazard is a potential source of harm. Risk analysis quantifies the harm.

Stage 1 comprises the design, planning, and development of training to be executed in actual employee training. Training design must comprehensively address the topic to be trained. Stage 1 design addresses new training as well as evaluating and integrating training changes identified during subsequent training stages. When the training process is designed, conscious thought about what might go wrong is integrated with the how and why being trained. Training design is the most important activity in the training process and may be complex depending on the subject topic. The following briefly describes elements of training designed in stage 1:

Content, presentation, testing, and process

. Numerous content categories (e.g., policies, procedures, equipment operation, analytical, etc.) are required for pharma employee training. Content presentation describes the method of delivering content; computer delivery (e.g., one-way delivery), virtual reality, Zoom interactions, classroom training, and skill delivery (i.e., on-the-job [OJT] training with a subject matter expert [SME]) are general categories. Testing to verify effective training is related to content complexity; “read and sign,” multiple choice, and skills demonstration to an SME are options.

 Supporting elements in training design include the trainer, trainee, and logistics. Background, technical expertise, and communication skills of trainers must be consistent with training complexity. Trainees must be capable of competent performance post-training. Training logistic considerations such as facilities and equipment, number of trainees per session, scheduling, internet access, and other relevant elements are specified in training design.

 All of the above must be recorded in an approved controlled document for use in future actual training. This document serves as an ongoing record of training subject matter. Document changes are controlled via change management; content is modified as experience is gained, and risks are evaluated. Supplementary information used in training to emphasize key content is appended.

 Training management is ultimately responsible for training design and related elements. Establishment of training standards for content, presentation, testing, trainers, trainees, logistics, and related elements is required. Standards must be documented. Standards are the backbone of the training effort that helps to establish a quality culture in the organization. Training compliance with standards should provide a quality message to trainees. A Training Master Plan listing training standards, approved Training Content Records, and other function information is recommended.

 Stage 1 hazards are identified during the design and development of the training content by the training author. Hazards are identified concurrently with the ‘what’ and ‘why’ of training; authors address ‘what might go wrong’ along with the what and why in subject matter. Addressing potential hazards within training leads to more effective content, presentation, and testing. Design personnel must anticipate potential problems during training design. Illustrations, videos, checklists, and other aids addressing problems should be incorporated into training design. Trainers must verbally emphasize critical content in their training presentation. Testing should emphasize questions on critical content. Addressing training problems through different types of learning (e.g., visual, auditory, reading/writing, kinesthetic, etc.) is recommended; employees learn in different ways. The greater the anticipated risk to product, process, or organization, the greater the potential problems, and the greater the effort that is required in training—a principle of ICH QRM. 

 provides examples of stage 1 hazards based on experiences and observations. 

 Examples of hazards in Stage 1 Training Design. P&ID is piping and instrumentation diagram.

Case study #1: deficient training design—machine setup.

 Customer complaints and returned product were received on a new prescription sedative product. Distinct white spots on the blue tablet surface were observed. Recall letters were sent and product was back-ordered with availability, financial, and regulatory ramifications. Engineers determined that milling equipment was incorrectly set up causing large white-spot particulate. Performance by manufacturing technicians was the hazard in this case; incorrect machine-part assembly was directly correlated to ineffective training. Improvements in training content and illustrations highlighting correct setup were developed. Trainees were required to demonstrate correct equipment assembly to an SME to successfully pass training.

Stage 2 training performance comprises the executed training of a trainee by a trainer and review of the training process. Stage 2 activities confirm the stage 1 design and identify necessary modifications—actual stage 2 training experiences between trainer and trainee may differ from stage 1 expectations. Stage 2 observation also affords opportunity to confirm trainer–trainee focus on critical aspects of the training subject matter:

 The actual training process executes the planned training designed in stage 1.

 Stage 2 training is evaluated; trainer, trainee, training management, training designer, and other interested individuals are involved. Overlooked or unanticipated problems are noted. Trainer emphasis on critical parameters is reviewed. Trainee engagement is evaluated.

Documentation—Personnel Training Record.

 The Personnel Training Record documents completion of training for employees. When an auditor asks to see the training record for a specific employee, this document will be provided. Training documentation contains dates and times of training sessions; these must be consistent with training design. If training is not clearly documented, it didn’t happen.

 Observation and evaluation of the training process by management provides an opportunity to enhance training design and performance. If changes are needed, the stage 1 Training Content Record is modified through the site change management process.

 Stage 2 hazards that were not anticipated in stage 1 design but become apparent during actual training are identified. New hazards noted in stage 2 are evaluated per ICH Q9(R1) methods and integrated per change management. Actual performance variation in trainers and trainees not anticipated in stage 1 design is noted. Training content, testing, logistics, and other considerations are modified in the Training Content Record via change management. 

 and case studies provide examples of potential stage 2 hazards. 

 Quality risk management (QRM) hazards in Stage 2 Training Performance. GMP is good manufacturing practice.

Case study #2a: training logistics—training in the auditorium. 

GMP) training for more than 200 site employees was conducted in response to a regulatory audit observation. The site auditorium was reserved, and a two-hour session was scheduled for 3 pm to include first and second shift employees. The session discussed data integrity, original data recording, error corrections, signature/date verification, “white-out” use, “post-dating,” and numerous other documentation topics. A few days after the training was completed, a site engineer was observed recording original data on scrap paper for later transfer to his notebook. Other technicians were observed writing data on “Post-it” notes instead of batch records. Continuing “white-out” use was also observed. Many key topics addressed in the training were not understood—training was an obvious failure. Poor training logistics was the fundamental hazard that caused ineffective training. Failure modes included too many people in the same room, a lengthy session at the end of the workday (i.e., sleeping employees), uncomfortable room temperature, smartphone distractions, and too many topics without adequate discussion. Trainer communication was also identified as a hazard; supplementary illustrations, lack of hardcopy handouts; and no Q&A with trainer were deficiencies. Training was poorly designed—-an efficient training session became more important than employee learning. Training was disingenuous; management just wanted it done to claim accomplishment of the regulatory commitment. Training was not given appropriate time, effort, and priority to accomplish employee learning. New sessions with smaller groups and improved presentation of content were developed and rescheduled. Large group auditorium training sessions were eliminated in future training.

Case study #2b: training logistics—computer fatigue. 

A site training program for “read and sign” training was managed through a training computer system. Employees were assigned training materials to be read; computer links were provided. Employees were allowed a 30-day time limit to complete the training; completion was self-paced. Employees would routinely give training a low priority and delay training until the deadline. This approach was not acceptable for new employees assigned an excessive number of training modules; more than 50 documents were often “read and sign” in one day—training was ineffective. Trained performance was the hazard identified in this case. The training computer system was modified to allow completion of not more than five training modules in one day.

A senior SME operator was the designated training person for a non-sterile manufacturing area. He always accomplished training of new personnel in one demonstration session. Some procedures required several hours of demonstration. The trainee and trainer then signed documentation that the trainee was qualified to perform the task without actual trainee demonstration of competency. When the newly qualified trainee was assigned manufacturing tasks, the senior person would be nearby to help as needed. Training deficiencies became apparent when the trainee was working alone. When the trainer was asked about his training methods, he responded that his manager told him to complete training as quickly as possible so that the trainee can begin working. Deficient technician performance was the hazard in this case. The trainer should have adjusted training repetition and emphasis based on trainee understanding and complexity of training content. After adequate repetition, the trainee should have been required to independently demonstrate trained skills. Training could not be firmly standardized due to the variation in trainee skills; experienced operators could be adequately trained by observation; inexperienced operators required additional repetition. Management did not give the priority to training due to production criticalities; production criticalities were more important than training. The manager had no incentive to make training a priority vs. his manufacturing responsibility; what the manager did undermined or undercut training. Future training required complete skills demonstration to an independent SME.

Continued training verification comprises the periodic review of training content and performance. Activities in stage 3 enable continuous improvement in training and maintenance of training quality. Training periodic reviews are specified in a Training Master Plan or other scheduling commitments:

 Training effectiveness is evaluated by review of experiential documents (e.g., deviations, non-conformances, investigations, corrective and preventive action [CAPA]), stakeholder interviews, trainer/trainee discussions, and other reviews that critically evaluate training performance. Investigations citing training deficiencies as causes of process deficiencies indicate the need for training improvements. Problem trends should be noted and addressed.

 Training content in the Training Content Record is reviewed for accuracy. Changes in equipment, processes, cleaning methods, and other variables may have occurred without concurrent changes in training. Supportive documents (e.g., flow diagrams, piping and instrumentation diagram [P&ID], sampling protocols, etc.) relevant to training may also be obsolete, inaccurate, or incomplete.

 Performance of trained procedures may also have been modified in response to equipment changes or improved efficiency without concurrent changes in training. Employee shortcuts in performing trained procedures are a common occurrence. Non-compliance with procedures are among the highest frequency of FDA-483 citations (18)

Documentation—Training Periodic Review.

 Performance of the Training Periodic Review must be documented.

 Completion of periodic training reviews as identified in the Training Master Plan or other commitment is the responsibility of management. If new hazards are identified, these must be submitted for evaluation through the QRM process and change management for implementation if appropriate.

 Stage 3 hazards may be identified in periodic review activities for a variety of reasons. New hazard identification may occur years after training was successfully completed. Stage 3 periodic review provides the opportunity to review ongoing routine performance of trained activities vs. approved training content, supporting materials, and procedures. Changes may have occurred in any of the above, making training inconsistent with aforementioned materials. For example, utilities may have been modified, supporting sampling diagrams may have changed, procedures may have changed, or practices may have changed without concurrent changes in training. Stage 3 hazards are also evaluated and integrated in stage 1 design per change management. 

 and case studies provide examples, experiences, and observations of potential stage 3 problems. 

 Quality risk management (QRM) hazards in Stage 3 Continued Training Verification. SME is subject matter expert. CAPA is corrective and preventive action. P&ID is piping and instrumentation diagram.

Case study #4: low tablet target potency. 

Periodic review of a tablet compressing process was conducted. The manufacturing technician was an excellent machine operator (“Best Technician Award”). His manufactured products always met acceptance specifications and product yields were always above average. Further evaluation of product potency data made under his responsibility indicated a slightly low average potency (97–98%) compared to lots made by other personnel. The manufacturing supervisor observed his machine setup and operation; tablet weight setup was within specifications, but always at a theoretical weight of less than 100%. When questioned why tablet weight was not adjusted to 100%, the technician responded that he always set a slightly low weight so that he would have a high product yield—management wants high yield and more profit—this is why he was “Best Technician.” Technician performance was the hazard in this case. GMP allows product release within specification (usually 95-105%), but product must be formulated to contain 100% unless otherwise justified. The technician was retrained on GMP requirements. Training documentation was revised to emphasize 100% formulation target requirements. Recognition procedures were modified to eliminate product yield as criterion for employee performance.

Case study #5: supporting documentation. 

Periodic review of site utility systems was conducted. Documents reviewed included procedures, supporting documents, and associated training. Supportive drawings and training documents were incomplete and not updated. Hazard in this case was inaccurate documentation. Investigation of discrepancies between documents indicated that final changes on drawings and training were not completed due to competing work priorities; responsible personnel were assigned other projects with higher priority. Review of other systems (e.g., compressed air, ethanol, and other utilities that had been recently modified) had similar inconsistencies—engineers were never allowed to completely finish a project before being reassigned by management to higher priority work.

Case study #6a: training culture—cleaning process review. 

Internal review of a product cleaning process was conducted in advance of a regulatory audit. Technical, Manufacturing, and quality assurance (QA) supervisors observed technician performance. Several discrepancies compared to approved procedures were noted. The amount of cleaning agent was modified; specific steps were changed to increase efficiency; an unspecified organic solvent was used to remove stains. When questioned about discrepancies, the manufacturing technician expressed site reality—the approved cleaning procedure was ineffective. Management advised to fix the cleaning problems without involving the technical group. The site culture was identified as the hazard in this case. The above occurrence demonstrated problems with interpersonal relationships, interactions between groups, function silos, and general disrespect for approved policies and procedures; technicians proudly stated, “We do whatever it takes to get the job done.” Management leadership and attitude were also identified as contributing to this hazard. The site quality culture begins with management (19). Several corrective actions were initiated within various functions at the site. Personnel learned to follow procedures and not “to do whatever it takes”.

Case study #6b: training culture—aseptic compounding. 

A sterile compounding facility required technician training and annual re-qualification on aseptic techniques. Testing required handwashing, garbing, work area sanitization, technique demonstrations, aseptic transfer of sterile media between vials, finger impressions testing, and related activities. Personnel observing testing noted unusually high frequencies of hand sanitization and surface sanitization by technicians; supervisors were continually reminding trainees to sanitize their hands. Most troubling was hand sanitization immediately prior to finger impression testing. The donning of new sterile gloves or spraying 70% alcohol immediately before finger-impression testing does not challenge aseptic technique and does not represent actual work performance. The site culture was identified as the hazard in this case. Technicians explained that the department manager told them, “There shall be no problems with qualification testing—do whatever necessary to prevent failures.” A deficient quality culture at the site translated into substandard employee behavior..

This paper discusses applications of QRM principles in a site training quality system. QRM applications enable several significant benefits to training.

 QRM in training focuses on identification of training problems. QRM application provides a distinct and comprehensive approach that complements traditional training. Training fundamentally comprises prospective activities (i.e., how to do things and why things are done). QRM provides additional relevant considerations to training—what can go wrong. QRM thus helps to describe training reality in the workplace. Hazard identification facilitates problem evaluation and subsequent action as needed.

 This discussion is structured according to the lifecycle approach as used in process validation. A lifecycle approach to training is described that affords opportunities to review and continually improve the training process. Three stages of QRM activities are identified, and QRM hazards are identified in each stage:

Stage 1 training design comprises planning and relates to hazards that are associated with training content, trainers, trainees, and the training process; stage 1 hazards are identified when new training is originally developed.

Stage 2 training performance relates to hazards associated with actual training activities when trainers present content to trainees; observation of stage 2 training may identify new hazards and necessitate modifications to content, trainer, or other designed elements.

Stage 3 continuous training monitoring (i.e., training periodic review) relates to hazards associated with the ongoing performance of trained procedures—training effectiveness, content accuracy, employee performance, consistency with related documentation, and compliance with approved procedures. Observation of training content and/or performance in stage 3 may also necessitate modifications to training content.

 illustrates the respective stages of the training lifecycle. Stages of the training lifecycle (black print) anchor the overall training process; hazards are originally identified in stage 1 when training is developed. If new hazards are identified in stage 2 or stage 3 (red print), these are forwarded to stage 1 for evaluation and integration with training content, presentation, testing, and other elements; training quality is maintained and continually improved by this process. 

 Quality risk management (QRM) integration into Training Quality System. Training lifecycle comprises Stages 1, 2, and 3 including management support. New training hazards identified in Stages 2 and 3 are evaluated and integrated within training as needed.

 Training management has significant responsibility and influence on performance of site training as follows:

 Training management is responsible for all training activities in all stages as well as for the general training function administration. Training is a cost; training management must balance accomplishing training objectives within site budgetary constraints. Numerous QRM hazards may be associated with management responsibilities. Management must integrate QRM considerations to maintain and improve training content. Management in this context includes training management as well as other site function management involved in training.

 A site training program often utilizes individuals from other site functions as trainers, SMEs, training content authors, or other contributors. These individuals also have direct responsibility for employee training. Training management must ensure these contributors know and understand their responsibilities, including QRM considerations, and that they are accountable for successful employee learning.

 Training management significantly influences the attitudes and performance of trained employees. Employee training is among the first activities of new employees; this relationship continues throughout the employee career with additional training, re-training, and other interactions. Management commitment to training excellence, including QRM considerations, must be visible and sets the tone for employee performance. Management actions influence the development of a quality culture in the organization.

Helpful discussions with Kevin O’Donnell, PhD, Health Products Regulatory Authority (HPRA) Ireland; Alan M. Mancini, RPh; and Richard Poska, RPh, managing director, Flexo CMC Consulting, Nokomis, Fla., are greatly appreciated.

1. ICH. Q9(R1) Quality Risk Management, Final version (January 2023).
2. Pappas, C. Eight Important Characteristics of Adult Learners. elearningindustry.com, May 8, 2013.
3. ICH. Q10 Pharmaceutical Quality System, Step 4 version (June 2008).
4. FDA. Guidance for Industry, Quality Systems Approach to Pharmaceutical CGMP Regulations (CDER, CBER, September 2006).
5. FDA. CDER Quality Management Maturity. fda.gov (accessed July 24, 2024)
6. Belyh, A. Management by Walking Around (MBWA)—The Essential Guide. cleverism.com, July 28, 2020.
7. FDA. 

Guidance for Industry, Process Validation: General Principles and Practices

 (CDER. CBER, January 2011).
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10. Pluta, P. L. Application of Validation Principles to the Training Quality System. Part 3—Trainers. 

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11. Pluta, P. L. Application of Validation Principles to the Training Quality System. Part 4—Trainees. 

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12. Pluta, P. L. Application of Validation Principles to the Training Quality System. Part 5—Training Process. 

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13. Pluta, P. L. Application of Validation Principles to the Training Quality System. Part 6 —Continued Training Verification. 

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14. Pluta, P. L. Application of Validation Principles to the Training Quality System. Part 7—Training Management Overview. 

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15. Pluta, P. L. Application of Validation Principles to the Training Quality System. Part 8—Training Process Documentation. 

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16. ICH. Quality Risk Management, ICH Q9(R1) Training Slides Hazard Identification—Part 1. 

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17. ICH. Quality Risk Management, ICH Q9(R1) Training Slides Hazard Identification—Part 2. Consideration in Relation to Identification of Failure Modes, in the Pharmaceutical Environment. 

 (accessed July 25, 2024).
18. Hoare, A. The Most Common FDA 483 Observations. 

, April 18, 2023.
19. Friedman, R. Establishing a Culture of Quality. 

Paul L. Pluta, PhD, RPh, is a pharmaceutical scientist with experience in pharma industry, university academia, pharmaceutical journal editorship, and community and hospital pharmacy practice.

®
Vol. 48, No. 11
November 2024
Pages: 20–26

When referring to this article, please cite it as Pluta, P. L. Application of Validation Principles to the Training Quality System: Part 9, Quality Risk Management Hazard Identification. 

Articles

Pharmaceutical training programs are enhanced by integrating ICH quality risk management considerations.

Application of Validation Principles to the Training Quality System: Part 9, Quality Risk Management Hazard Identification

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Aptamer Group announced on Nov. 7, 2024 that one of its customers has advanced use of the company’s Optimer binders for the commercial phase of its product. The customer has used the Optimer delivery vehicle for the product’s development phase and Aptamer shipped test materials to the customer in October 2024.

According to Aptamer, the customer has validated the performance of the delivery vehicles, indicating the vehicles can target the required cell type. The vehicles have the potential to be used as part of a precision therapy. This would create downstream licensing revenue for Aptamer.

The customer also found that Optimer showed binding to the target cells from a panel of animal species, which makes the delivery vehicle desirable for preclinical development. “Due to the targeting specificity, multiple species binding, and reproducibility of the results, the customer has committed to progressing to the final commercial development stage for the therapeutic delivery vehicle,” Aptamer stated in a press release (1).

According to Aptamer, targeting genetic medicines to specific cell types is a challenge. Highly selective delivery vehicles address this challenge by targeting these cell types, which has been difficult in the past. The company is developing delivery vehicles that target fibrotic liver cells, creating progress for genetic medicine payloads.

“We are delighted with our customer’s decision to advance to the final stage of Optimer development. The customer’s results align with those seen within our own laboratories and validate the hard work of our scientific team to develop novel, high-performance delivery vehicles,” said Arron Tolley, chief executive officer of Aptamer Group, in the press release. “As this Optimer advances through the final development stages, we continue to move closer to demonstrating the strength of our Optimer platform in generating high-value, therapeutic targeting assets. This represents our second drug delivery asset with licensing potential, alongside our liver fibrosis targeting Optimer, which has already shown good effectiveness in delivering functional genetic medicines to specific cells. These could enable new approaches to precise drug delivery across the field of genetic medicines whilst underpinning our business model with passive income from licensing. We look forward to updating shareholders on future progress in due course.”

Earlier in 2024, the company announced an agreement with AstraZeneca to evaluate the use of Optimer for targeted delivery of small interfering RNA (siRNA). Aptamer will conduct in-house experiments to assess the effectiveness of the delivery vehicle with AstraZeneca’s siRNA as part of the agreement.

“Delivery of siRNA to precise cell types and tissues with successful cell internalization remains a significant challenge for the wider therapeutic application of the technology. Despite this limitation, the siRNA market was still valued at over $13 billion in 2023. Optimer technology could represent a paradigm shift in the targeted delivery of siRNA molecules, due to the high levels of selectivity, high affinity, and simple conjugation of siRNA payloads, offered using Optimer delivery systems as non-viral vectors. If successful, Optimer-enabled delivery of siRNA could lead to the development of novel compounds that have significant advantages over current cell and tissue-targeting methods,” the company stated in a press release (2).

“We are really excited to work with AstraZeneca to evaluate and optimize our Optimer delivery vehicles,” said Tolley in the release. “Partnerships such as this will enable Aptamer Group to make rapid progress in this area, which is a key focus for Optimer technology. Targeted delivery to specific cell types remains a critical unmet need in many applications within the tissue targeting space and, as such, has been part of our strategic focus to support the development of binders with the potential for long-term, high value. Our initial dataset in Optimer targeted delivery has raised significant interest from multiple parties. We are eager to advance to animal model testing and delighted to continue collaborating with AstraZeneca in this endeavor. Reaching proof of principle in animal models will derisk the Optimer delivery platform and bring us closer to delivering targeted and effective gene therapies for patients.”

Aptamer Group. Aptamer Extends Agreement with Genetic Medicines Customer. Press Release. Nov. 7, 2024.

Aptamer Group. Agreement to Explore Optimer Vehicles for Targeted Delivery of siRNA. Press Release. July 3, 2024.

A genetic medicines customer has selected the company’s binders for commercial development of the customer’s product.

Aptamer Extends Agreement for Drug Delivery Vehicles

Avid Bioservices, a biologics contract development and manufacturing organization (CDMO), announced on Nov. 6, 2024 that it has entered into a merger agreement, valued at approximately $1.1 billion, with GHO Capital Partners and Ampersand Capital Partners.

Under the terms of the agreement, GHO and Ampersand will acquire all outstanding shares held by Avid’s stakeholders for $12.50 a share in cash, which is a 13.8% premium to the Nov. 6, 2024 closing price of $10.98 and a 21.9% premium to Avid’s 20-day volume-weighted average share price. The transaction is expected to close in the first quarter of 2025, subject to customary closing conditions, approval by Avid’s stockholders, and receipt of required regulatory approvals.

“Since our founding, Avid Bioservices’ business has grown by evolving to meet our customers' broad range of development and manufacturing needs. After years of investment and expansion, now is the right time to move forward as a private company with new owners that will support our next phase,” stated Nick Green, president and CEO of Avid Bioservices, in a press release (1). “In evaluating this transaction, our [b]oard considered a range of alternatives and determined that it provides our stockholders significant, immediate, and certain cash value for their shares. Partnering with GHO Capital and Ampersand Capital Partners allows us to build on our strong foundation by accessing their significant knowledge base, network and capital to position the business for the future with our customers.”

“We are excited to announce this recommended cash acquisition of Avid,” said Alan MacKay and Mike Mortimer, managing partners of GHO, in the release. “As experienced CDMO industry investors, GHO brings deep expertise and experience to support Avid's management team going forward. Our mission at GHO is to make healthcare better, faster, and more accessible and at the heart of this is enabling efficient, high-quality manufacturing of innovative treatments. Avid exemplifies this perfectly—the [c]ompany operates in high-growth markets, producing complex biologics for leading pharmaceutical and biotech innovators at both the clinical and commercial stages. Avid’s recent investments, both in capacity and its exemplary team, position it strongly for future growth. We look forward to working with the Avid team to unlock the [c]ompany’s full potential through our established playbook of expanded offerings, talent investment, and greater geographic reach.”

“Avid has long been a trusted provider of biopharmaceutical development and manufacturing services, and we have tremendous respect for its team’s expertise, its broad spectrum of customized services, and its strong regulatory track record. We look forward to leveraging our deep industry experience, focused strategy, and collaborative approach to drive growth,” said,David Anderson, general partner ofAmpersand, in the release.

Avid offers a range of biopharmaceutical process development, current good manufacturing practice (CGMP) clinical, and commercial manufacturing services, which include CGMP clinical and commercial drug substance manufacturing, bulk packaging, release and stability testing, and regulatory submissions support. It also provides process development activities for early stage programs, such as cell line development, upstream and downstream development and optimization, analytical methods development, testing, and characterization.

1. Avid Bioservices. Avid Bioservices to be Acquired by GHO Capital Partners and Ampersand Capital Partners in $1.1 Billion Transaction. Press Release. Nov. 6, 2024.

The merger agreement is valued at approximately $1.1 billion.

Avid Bioservices Acquired by GHO Capital Partners and Ampersand Capital Partners

Cynthia A. Challener is a contributing editor to Pharmaceutical Technology.

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High-throughput screening (HTS) enables the rapid testing of numerous compounds against biological targets. By automating and miniaturizing assays, HTS significantly accelerates the identification of potential drug candidates or optimal process conditions and can be applied throughout the entire development path of a potential drug, from discovery to process development. In the discovery phase, HTS is one of several hit-identification (Hit-ID) paradigms and involves the use of sensitive and miniaturized methods for screening large libraries of small molecules in a practical and efficient manner. During process development, HTS helps researchers rapidly establish an optimal synthetic route and conditions for each process step that reliably produce sufficient material quickly.

The most widely used HTS technologies for identification of small-molecule API candidates include fluorescence-based, luminescence-based, and absorbance-based assays, according to Sachin Mahale, research leader, Discovery Sciences at Charles River. He adds that methods for detection of unlabeled biomolecules, such as those based on mass spectrometry, have also become more widely used at an HTS-scale in recent years, allowing the screening of compounds in both biochemical and cellular settings and thus expanding the breadth of targets to those that would otherwise be unscreenable. High-content imaging and automated electrophysiology are additional techniques providing multi-parametric cellular data that are becoming more widespread in high-throughput formats for discovery research, Mahale observes.

“Considering the vast number of technologies, each one of them serving a different purpose to address the scientific question, naturally different attributes, including binding affinities, reaction kinetics, enzymatic activity, functional activity, target engagement, selectivity, and specificity, are often analyzed. These attributes are critical because they provide early insights into the potential efficacy and selectivity of drug candidates, which is essential for optimizing pharmacological profiles,” says Mahale. Given the wide range of technologies available, he also emphasizes the importance of selecting a fit-for-purpose technology to address each biological question.

Numerous advantages for process development

During the early stages of candidate development, the synthetic pathway is still flexible, making HTS particularly valuable, according to Jens Schmidt, associate director of manufacturing, science, and technology at Lonza. “HTS can help process development scientists rapidly evaluate the feasibility of different synthetic routes or optimal chemical combinations, such as solvents, catalysts, and bases,” he explains. As the project advances through clinical phases, the flexibility in process steps decreases, but HTS remains useful for expanding the understanding of the process, such as its reaction kinetics.

Overall, using HTS during process development can, contends Schmidt, “help in the optimization and simplification of established workflows, integration of emerging technologies such as photochemistry and electrochemistry, and leveraging of rapidly evolving computational tools like artificial intelligence (AI) and machine learning (ML).”

One of the key advances in HTS for drug discovery and development is the adoption of a wider array of state-of-the-art analytical techniques for this application. The most notable technologies introduced recently as high-throughput platforms include affinity selection mass spectrometry (ASMS)-based screening platforms such as self-assembled monolayer desorption ionization (SAMDI), target protein degradation (TPD), and molecular glue platforms; and CRISPR (clustered regularly interspaced short palindromic repeats)-based functional screening platforms, notes Luke Alderwick, senior group leader, Discovery Sciences with Charles River.

ASMS screening is increasingly used to discover small molecules that engage a specific target, says Alderwick. “Highly advanced ASMS platforms that use rapid workflows to quickly generate high-quality data are getting traction as HTS methods. The platform is amenable to a broad spectrum of targets, including proteins, complexes, and oligonucleotides such as RNA, and can be a leading assay to initiate drug discovery programs,” he comments. Unlike traditional label-free approaches, which are often slow, restrictive, and not optimal for large screening campaigns, Alderwick adds that new solutions such as Charles River’s SAMDI ASMS platform allow efficient investigation of protein-protein interactions, TPD, molecular glues, RNA binders, etc. in a high-throughput format.

CRISPR-based functional screening, meanwhile, is employed to elucidate the biological pathways involved in disease processes, according to Alderwick. CRISPR editing of genes for knock-out and knock-in experiments have greatly advanced assay designs for cell-based phenotypic HTS approaches, he explains. In addition, by selectively tagging proteins of interest, CRISPR is advancing the understanding of target engagement and functional effects of drug treatments. “By screening small molecules alongside CRISPR-modified cells, researchers can better understand drug-target interactions at a genomic level, enabling the selection of candidates with higher precision,” says Alderwick.

Overall, Alderwick summarizes, these various innovations have improved the scalability, specificity, and throughput of HTS, enabling researchers to screen more diverse compound libraries and discover novel molecular mechanisms of action.

Automation and miniaturization technologies having an impact

Some of the biggest advances improving and expanding HTS for drug discovery and development relate to automation and miniaturization of laboratory systems. “Automation is transforming the landscape of API development and manufacturing,” Schmidt states.

Automated liquid-handling robots, says Mahale, have become faster, more accurate, and capable of working with extremely low volumes of reagents, enabling more cost-effective screening. “Advances in pipetting precision, multi-plate handling, and integration with other systems (like incubators, centrifuges, and imagers) have significantly reduced human error and improved reproducibility. Automated workflows for complex cell assays and readouts based on imaging analysis, meanwhile, allow multiple assays to be performed with minimal consumable cost and keeping sustainability in mind. Use of multimode plate readers in the automation workflow also allows for multiplex assays to maximize data collection,” he details.

By automating process development tasks, researchers can significantly accelerate the identification of optimal synthetic pathways and fine-tune processes for maximum efficiency, according to Schmidt. “This translates to faster development times, reduced errors, and more consistent API production. For drug developers, this means reduced risk and uncertainty in API development, enabling a greater focus on clinical trials and regulatory approvals,” he contends.

More specifically, automation accelerates workflows by allowing researchers to complete multiple tasks at once. “This kind of parallelization is especially helpful for processes like catalyst screening, ligand screening, or solvent selection. Automation also allows chemists to conduct experiments in high-pressure and high-temperature conditions, which are difficult to complete manually. With these features, automation makes experimentation in the lab more targeted and efficient, saving valuable time, energy, and cost,” Schmidt notes.

In addition to advances in automation, innovation in miniaturization technologies is helping to improve HTS performance. “By shrinking assay volumes, miniaturized platforms like microfluidics and nanodispensing have allowed for higher throughput while reducing reagent consumption,” Mahale remarks. Access to smaller-volume assays, he says, is especially useful in fragment-based drug discovery and the screening of complex biological samples, as it allows for cost-effective experiments with even larger libraries.

Data analysis and sharing solutions making a difference

In addition to innovation in the analytical methods and laboratory equipment used for HTS, significant advances have also occurred in the software arena, most notably the integration of AI and ML for data analysis and improvements in cloud-based platforms for collaborative research.

New AI algorithms can, observes Schmidt, analyze data from high-content screening systems, detecting complex patterns and trends that would otherwise be challenging for humans to identify. AI/ML algorithms can identify patterns and predict the activity of small-molecule candidates, even when the data are noisy or incomplete, Mahale adds. In addition to significantly speeding up the analysis of screening results and identifying promising leads faster and with higher accuracy, integration of AI into HTS processes can also improve assay optimization, he comments. Another key advantage of AI-driven HTS, according to Mahale, is its ability to adapt to new data in real time when compared to traditional HTS relying on pre-determined conditions.

“These advances in AI and ML have been pivotal in accelerating drug discovery by improving both the quality of data and the efficiency of experimental processes. In the early stages of development, meanwhile, using these advanced solutions provides a holistic view that streamlines route scouting and accelerates the transition from research and development to robust manufacturing processes,” Schmidt says. As an example, he points to Lonza’s AI Route Scouting Service, which integrates proprietary commercial data with cutting-edge synthesis planning technology. “This innovation allows for more accurate and comprehensive analysis of cellular phenotypes, providing deeper insights into the efficacy and safety of drug candidates,” he notes.

Cloud technology, meanwhile, has enhanced the ability to store, share, and analyze HTS data across multiple sites in real time, says Mahale. “The result is improved collaboration between research teams globally, reduced data redundancy, and increased application of machine learning models on large datasets,” he explains.

Combined, Mahale believes the various software innovations incorporated into HTS systems are enabling the handling of more complex datasets, improving prediction accuracy and streamlining the drug discovery pipeline.

Implementing HTS for drug discovery and development is not a simple matter. The systems are both complex and costly. Fortunately, minimizing these issues has also been a focus of technology providers and end users.

Schmidt points to significant advancements in standardization and simplification of workflows as helping to accelerate HTE experimentation timelines and reduce costs. Examples include the development of standardized protocols and data formats, which has simplified the implementation and operation of HTE assays, specifically improving their accessibility and accuracy. He also notes that software tools powered by AI have made it easier for researchers to design, execute, and analyze HTE experiments.

Another effective approach, according to Alderwick, is to leverage tailored compound libraries such as Charles River’s Lead-Like Compound Library, which include compounds with lead-like properties and diversity and excludes problem chemotypes. The company’s Fast-Track HTS offerings leverages this library to provide rapid hit discovery and lead identification while maintaining high-quality data outputs, he adds. “With a fixed-cost and streamlined program, this service is ideal for proof-of-concept studies and suits research groups or companies with limited funding because it allows for high-quality, early-stage screening without the need for extensive internal resources or infrastructure,” Alderwick says.

Alderwick also notes that modular systems in development offer the potential to customize HTS systems to the specific needs of a lab, which will further lower the initial capital investment required. “All of these advances are making HTS more cost-effective, scalable, and user-friendly, reducing barriers to entry for smaller research organizations,” he concludes.

Despite the many remarkable advances made in HTS technologies for drug discovery and process development, significant challenges impacting both the efficiency of identifying optimal small-molecule candidates and processes, and their downstream success in clinical trials, remain.

One important issue, according to Schmidt, is the need to analyze large HTE datasets that require significant computational resources. “This can be a bottleneck for many drug developers, which is why partnering with a contract development and manufacturing organization (CDMO) like Lonza can be helpful. CDMOs have the tools and technology, such as AI algorithms, to help drug developers analyze data and progress through development stages quickly,” he says.

There are also challenges to handling very small amounts of solids, Schmidt notes. “Although this may seem like a minor technical detail, understanding and accepting these limitations is crucial,” he says. One potential solution highlighted by Schmidt is the use of solid supports such as ChemBeads (MilliporeSigma), small, chemical-coated glass beads created specifically for use in HTS applications.

A primary challenge in HTS for drug discovery is identifying assays that are physiologically relevant to the disease state despite the need for simplification to ensure scalability and robustness, according to Mahale. Model systems do not always translate well. In addition, biochemical assays, while simpler, often fail to capture the nuances of cellular contexts, such as the impact of membrane permeability, metabolite production, or protein-protein interactions, while cell-based assays are closer to physiological reality but are more complex, expensive, and challenging to miniaturize.

“The complexity of human diseases, particularly multifactorial diseases like cancer or neurodegenerative disorders, makes it difficult to develop assays that can recapitulate the true pathophysiology of the disease,” Alderwick observes. “Poor assay choice,” he continues, “leads to the identification of hits that might show promise in early testing but later fail due to lack of efficacy or off-target effects in more complex disease models or clinical settings. The incorrect assay not only risks identifying false positives but also misses out on potential therapeutic candidates that could have better real-world efficacy. This limitation significantly contributes to the high attrition rates in drug development.”

Another challenge is the development of a robust screening cascade (primary, secondary, and tertiary screenings) that helps to identify, confirm, validate, and refine viable candidates and reduce the attrition rate during later stages of drug development. Further assays are then employed during lead optimization to assess toxicity, selectivity, functional activity, bioavailability, and pharmacokinetics for further hit validation. “Failures in designing an appropriate HTS cascade occur when these different stages are not sufficiently de-risked,” contends Alderwick.

While incorporating more predictive absorption, distribution, metabolism, excretion, and toxicity screens early in the process has helped de-risk candidate selection to some degree, the challenge still lies in balancing the need for throughput and speed with the inclusion of more complex, multi-parametric data, according to Alderwick. “Early prediction of 

 efficacy and safety in HTS remains a weak point, contributing to high failure rates in the transition from hit-to-lead and lead-to-clinical candidate stages,” he concludes.

Finally, Mahale notes that the selection of chemically diverse and high-quality libraries for HTS is pivotal in the success of small-molecule candidate identification, because even with the best assays, if the chemical library lacks diversity or contains unsuitable and non-drug-like compounds, the likelihood of finding optimal candidates diminishes. “Many historical compound libraries are biased toward certain chemical scaffolds that have been repeatedly screened over decades, thus reducing the chances of discovering novel small molecules with new mechanisms of action,” he observes. Charles River, Mahale adds, has tackled this issue by developing an expertly curated small-molecule compound library that covers a wide range of structural and functional diversity, thereby maximizing the probability of finding useful hits.

Several emerging strategies aim to improve the efficiency, reliability, and outcomes of HTS by leveraging advancements in technology, biology, and chemistry, according to Alderwick. Advances in organoid and 3D-cell culture technologies are helping to bridge the gap between simple 

 assays and complex human disease biology, and thus address the need for more physiologically relevant assays, he observes. “These models better mimic the physiological conditions of tissues and organs, providing a more relevant system for screening potential drug candidates,” Alderwick explains. Organoids derived from patient tissues, for example, can offer insights into how drugs might work in specific disease contexts, improving the predictive power of assays.

To shrink the gap between biochemical, cell-free assays and phenotypic, cell-based screens and thus better understand the on-target activity of identified hit molecules, including binding assays such as for target engagement and protein-protein interaction analysis in the HTS screening cascade, would greatly advance the ability to select optimal compounds for further development, believes Alderwick. He also notes that the use of advanced cellular models, such as CRISPR/Cas (

-associated protein) knockouts and induced pluripotent stem cells, for early screening is likely to improve HTS outcomes.

Continued advances in automation and the use of AI and ML will further enable HTS platforms to handle more complex assays with higher precision, reproducibility, and throughput, according to Mahale. “Fully automated HTS systems can reduce human error, improve the consistency of assay conditions, and allow for more sophisticated workflows that balance throughput with data quality, while incorporation of AI/ML algorithms can reduce the rates of false readouts, making HTS more reliable and reducing the time spent following up on false positives,” he says.

Ultimately, concludes Mahale, these emerging solutions aim to reduce the high attrition rates in drug discovery by improving the relevance and quality of the screening process, optimizing candidate selection, and expanding the chemical diversity available for exploration. “While challenges remain, these developments promise to significantly enhance the utility of HTS in the drug development pipeline,” he contends.

Looking beyond these strategies, Alderwick is excited about the trend toward integration of multi-omics approaches into HTS, which he says represent a major leap forward in the ability to identify optimal small-molecule API candidates. “Multi-omics approaches leverage data from various molecular levels, including genomics, proteomics, transcriptomics, metabolomics, and even epigenomics, providing a more comprehensive and holistic understanding of how small molecules interact with entire biological networks and pathways rather than isolated targets,” he explains. “Complex diseases such as cancer, neurodegenerative disorders, and metabolic syndromes often involve multiple, interconnected biological systems, and thus multi-omics approaches have a greater likelihood of identifying effective new drug candidates,” he states.

In today’s fast-paced drug manufacturing environment, HTS has become an indispensable tool. However, Schmidt emphasizes that advances in technology are not sufficient by themselves to enhance the performance of these platforms. “Looking ahead, we can expect continued improvements in both reliability and simplicity, but the key to success lies in having a motivated, dedicated, and technically skilled team that is ready to embrace challenges and open to adopting innovative techniques,” he insists. 

Cynthia A. Challener, PhD, is a contributing editor for 


Vol. 48, No. 11
November 2024
Pages: 12–16

When referring to this article, please cite it as Challener, C. Accelerating Discovery and Development with Advances in High-Throughput Screening. 

Automation, miniaturization, and new software algorithms are improving throughput and accuracy.

Accelerating Discovery and Development with Advances in High-Throughput Screening

Feliza Mirasol is the science editor for 

On Oct. 30, 2024, Lonza's Synaffix announced that it has licensed its antibody-drug conjugate (ADC) technology to BigHat Biosciences (BigHat), a US-based biotech company. BigHat will combine the Synaffix technology with its machine learning (ML) antibody design platform to develop a new ADC pipeline program.

Under the agreement, BigHat will receive target-specific access to Synaffix’s late clinical-stage, site-specific ADC technology platform. This platform enables transformation of antibodies into proprietary, best-in-class ADC products. Lonza’s acquisition of Synaffix in June 2023 (1) means that BigHat also gains access to Lonza’s DNA-to-investigational new drug (IND) ADC offering. This DNA-to-IND offering enables drug developers to receive all necessary current good manufacturing practice (CGMP)/non-CGMP ADC batches leading up to the first clinical trial. The service is housed under a single quality system that encompasses all critical development and manufacturing activities, including antibody production, bioconjugation, and drug product filling.

“The ADC space continues to develop and evolve, and our collaboration with BigHat exemplifies the continued strength and appeal of our best-in-class ADC technology. We are excited to be at the forefront of next-generation ADC R&D by joining forces with BigHat and its AI [artificial intelligence]/ML-enabled antibody discovery and development platform. BigHat is the ideal partner for Synaffix to collaborate with. We look forward to supporting BigHat with every step in the ADC development and manufacturing process under the Lonza umbrella,” said Peter van de Sande, head of Synaffix, in a company press release.

Milliner, which is BigHat’s AI/ML-powered antibody design platform, works by integrating a synthetic biology-based high-speed wet lab with state-of-the-art ML technologies into a full-stack antibody discovery and engineering platform. This platform is used to engineer antibodies with more complex functions and better biophysical properties. Using this approach reduces the difficulty of designing antibodies and other therapeutic proteins when developers look to tackle conditions ranging from chronic illness to life-threatening disease. The platform also offers significant speed to reaching candidate discovery and validation.

“We are pleased to announce this collaboration with BigHat for its next-gen ADC molecule, which shows how drug developers can benefit from a fully integrated suite of services across our Biologics division. The Lonza network has continued to adapt to the evolving ADC landscape, including the recent addition of a highly potent vial fill capability at our Stein (CH) [Switzerland] facility. This dedicated ADC drug product capacity expansion completes our offer for comprehensive, end-to-end development and manufacturing services for ADCs, simplifying the path from DNA-to-IND and beyond for our customers,” Jean-Christophe Hyvert, president, Biologics, Lonza, said in the release.

Through this collaboration with Synaffix/Lonza, BigHat intends to advance its next-gen ADC program. BigHat’s ADC program is at the IND-enabling stage and is on track to be the company’s first clinical-stage program. According to the press release, BigHat’s next-gen ADC was optimized using the Milliner platform. It is designed for maximum payload delivery to tumor cells and with optimal drug-like-properties. Incorporating Synaffix's ADC technologies, such as GlycoConnect, HydraSpace, and toxSYN, BigHat expects to further improve the safety and efficacy of its next-gen ADC.

"BigHat is leveraging our AI-driven antibody design platform, Milliner, to develop advanced biologics that provide safer and more effective treatments for difficult-to-treat cancers. We are excited to combine Synaffix’s conjugation and linker-payload technologies with our custom-designed antibody to create a next-generation ADC for patients with high unmet medical need," said Mark DePristo, CEO, BigHat Biosciences, in the release.

1. Lonza. Lonza to Acquire Synaffix and Strengthen Antibody-Drug Conjugates Offering. Press Release. June 1, 2023.

Synaffix, a Lonza company, has licensed its ADC technology to BigHat Biosciences to be combined with the latter's ML design platform to develop new ADCs

Synaffix, a Lonza Company, Collaborates with BigHat Biosciences on ADCs Designed Using Machine Learning

Chris Burgess, PhD, is an analytical scientist at Burgess Analytical Consultancy Limited, ‘Rose Rae,’ The Lendings, Startforth, Barnard Castle, Co Durham, DL12 9AB, UK; Tel: +44 1833 637 446; chris@burgessconsultancy.com; www.burgessconsultancy.co

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Nomenclature is important. Data governance, data integrity, and data quality are all widely used terms, but what do they actually mean and how are they connected? The purpose of this article is to provide a structured model for these terms with their definitions and their relationships in the context of analysis and testing within a pharmaceutical quality system.

The crucial concept is that data quality, including data integrity, is only attainable via data governance, as will be illustrated in the proposed model.

In a regulatory context, the establishment of measured values and subsequent reportable results of a predefined quality is an essential activity. Reportable results are then compared with predetermined acceptance criteria and/or standards and specifications. The processes, mechanisms, and control systems necessary to establish measurement values and reportable results of a defined quality are interlinked. These interlinkages may include metrological, procedural, or organizational elements.

The overall proposed model is built up using building blocks akin to the construction of Lego brick models. This approach has been used in previous papers concerning error budgets in measurement uncertainty and Monte Carlo Simulation, and data quality within a lifecycle approach (1–2).

An analytical testing flow diagram, shown in 

, gives a high-level idea of the traceability and subsequent interactions from the marketing authorization (new drug application, NDA, or abbreviated new drug application, ANDA) to the reportable result and data quality.

 A high-level process flow indicating some of the elements of data governance, data quality, and data integrity, as well as the quality management system (QMS).

This process flow has been translated into the Lego brick model shown in 

. This model is based upon a data quality within a lifecycle approach model which has been modified and extended, as will be described in some detail (2).

 Data governance model modified and extended from the data quality within a lifecycle approach model (taken from reference 2, Figure 15).

The crucial concept is that data quality is only attainable via data governance.

The data governance and data quality Lego brick model

Data quality is not an accident but a product of design. It is a combination of data integrity and the functionality and control under the Pharmaceutical Quality System, usually termed the quality management system (QMS). These aspects are underpinned by data governance and qualified information technology (IT) infrastructure services. This is essentially a sandwich structure in which the “filling” is provided by metrological integrity, analytical procedure integrity, and quality oversight. These “fillings” are described below.

 are subject to risk assessments and risk management (3), which will not be discussed here.

The qualified IT infrastructure services with cybersecurity and access control, as seen above, also will not be covered further in this article.

It is, however, necessary to discuss the key elements of data integrity and the Pharmaceutical Quality System which, when combined, generate data quality on the foundation of data governance.

Data governance is the totality of arrangements to ensure that data, irrespective of the format in which they are generated, are recorded, processed, retained, and used to ensure a complete, consistent, and accurate record throughout the data lifecycle (4).

The purpose of an analytical procedure is to provide a reportable result of the analytical characteristic or quality attribute being determined. Analysis and testing require a measurement system, and a procedure for its application to a sample.

Data integrity is underpinned by the first brick, the metrological integrity of the instrument or system’s operational performance, with demonstrable assurance that it is “fit for intended use” within a specific analytical procedure, which is “fit for intended purpose” over the data lifecycle.

Data governance, data integrity, and data quality are all widely used terms, but what do they actually mean and how are they connected?

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