Science Pool

Read about the newly adopted harmonized drug interaction guideline including:

• An overview of the key highlights of the new ICH M12 harmonized guideline on drug interaction studies (2024)
• A comparison with the EMA guideline on the investigation of drug interactions (2013)

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ICH M12 2024 vs FDA 2020 DDI Guidance. Exploring the Differences.

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The new ICH M12 guideline on drug interaction studies is welcomed by industry as it provides a harmonized approach which is expected to be implemented by the major regulatory authorities such as the US Food and Drug Administration (FDA), European Medicines Agency (EMA) and the Japanese Pharmaceuticals and Medical Devices Agency (PMDA) and presumably replace their respective existing guidance. In this blog, we explore the differences between the now finalized ICH M12 guideline adopted in May 2024 (draft for comment released 2022) and the previous US FDA 2020 guidance released in January 2020 with a focus on the in vitro assays.

Before we launch into the individual assays, one general observation in the new ICH M12 guideline is the replacement of the term ‘victim’ to ‘object’ to denote an investigational drug (usually a substrate) which is affected by a concomitant drug, and the replacement of the term ‘perpetrator’ to ‘precipitant’ to denote an investigational drug (typically an inhibitor or inducer) which affects a concomitant drug. This amendment only appeared in the final version of the ICH M12 guideline as it was one of the comments from review of the draft version.

Reaction Phenotyping

If we consider reaction phenotyping initially, the differences between the new ICH M12 and the FDA 2020 guidance are relatively minimal. Although the ICH M12 refers to all the enzymes mentioned in the FDA guidance, it also refers to some additional Phase II enzymes which may need to be evaluated including glutathione S-transferases (GSTs) and N-acetyl transferases. Both the ICH M12 and the FDA guidance have the same cut-off of ≥25% of total elimination identified using in vitro studies (reaction phenotyping and metabolite identification) and human mass balance data for determining if the enzyme needs further investigation in a clinical study. One key difference is that the FDA guidance recommends two methods should be used for reaction phenotyping – the first method using selective inhibitors in the presence of human liver microsome or hepatocytes and the second method using human recombinant enzymes. In the ICH M12 guidelines, only one of these methods is required.

Both the ICH M12 guideline and the FDA guidance have recommendations for evaluating metabolites in reaction phenotyping studies if the exposure levels of the metabolite results in clinically relevant changes in efficacy or safety.

Enzyme Inhibition

Moving onto enzyme inhibition, both the ICH M12 and FDA guidance recommend evaluating the main seven CYP isoforms for reversible and time dependent inhibition. For reversible inhibition, the cut-off for determining if a clinical study is required is the same for both the ICH M12 and FDA. However, for time dependent inhibition, 5 x C_max,u is used in the calculation in the ICH M12 whereas a higher safety factor of 50 x C_max,u was used in the 2020 FDA guidance, suggesting a less conservative approach is used in the new ICH M12 guideline.

One addition to the ICH M12 guideline, which is not specifically included in the previous FDA 2020 guidance, is the inclusion of reversible UGT inhibition. The ICH M12 recommends that UGT1A1, UGT1A4, UGT1A9, UGT2B7 and UGT2B15 inhibition should be evaluated if direct glucuronidation is one of the major elimination pathways of the investigational drug. Although it is considered an area of ongoing research, it is currently recommended in the ICH M12 that the same cut-off values should be used for UGT inhibition that is applied to CYP enzymes (i.e., C_max,u/K_i,u<0.02).

Enzyme Induction

For enzyme induction, the FDA guidance suggested either catalytic activity or mRNA could be used to assess induction. It also suggested that immortalized hepatic cell lines may be used to determine CYP induction potential. However, the new ICH M12 primarily recommends the analysis of mRNA (with the exception of CYP2C19 where catalytic activity should be measured) and that the CYP induction assessment should be performed in human hepatocytes. The ICH M12 guideline also addresses the issue of toxicity and recommends that cell viability assessment is performed before and at the end of the incubation.

For data analysis, both the ICH M12 and FDA cover the three basic methods (fold-change, relative induction score correlation method and basic kinetic model) and align on the cut-offs for indicating if an investigational drug has the potential to induce in vivo. However, for the basic mRNA fold method in the ICH M12 guideline describes assessing test drug concentration of 50 x C_max,u whereas the FDA only suggests testing up to 30 x C_max,u suggesting a more conservative approach now by the ICH M12. Furthermore, in the calculations for the correlation methods and the basic kinetic model, unbound EC₅₀ (EC_50,u) is specified in the ICH M12 whereas only EC₅₀ is referred to in the FDA guidance. Finally, the ICH M12 describes in more detail an indicative positive control response for CYP1A2, CYP2B6 and CYP3A4 of typically at least 6-fold to ensure sufficient sensitivity of system, whereas this level of detail is absent from FDA.

Transporter Substrate Identification

Both the ICH M12 guideline and the FDA DDI guidance (2020) recommend the same transporters are assessed. The method for testing and thresholds for clinical assessment are very similar between the FDA and ICH M12.

Transporter Inhibition

For transporter inhibition, the same transporters are recommended in the ICH M12 as the previous FDA guidance, however, the cut-off value for determining if a clinical study should be performed are different in a couple of instances. Firstly, for P-gp and BCRP inhibition where the investigative drug is administered by the parenteral route or if a metabolite is formed post absorption, the cut-off value in ICH M12 is an IC₅₀ of 50 x C_max,u, whereas in the FDA guidance it is 10x C_{max total}; important to note the additional difference for what is defined as [I]. This indicates a more conservative approach by the FDA when plasma protein binding is > 80%, or by ICH M12 when plasma protein binding is < 79%. The second instance applies to the cut-off value for MATE1 and MATE2-K, which has increased to an IC₅₀ of 50 x C_max,u rather than 10x C_max,u, indicating a more conservative approach by the ICH M12 for these renal transporters. The ICH M12 also specifically refers to unbound IC₅₀ (IC_50,u) in the calculations whereas the FDA guidance only references IC₅₀. In this regard, it is important to recognize that any correction of IC₅₀ for potential non-specific binding that might occur within a transporter inhibition assay would only be required if the assay did not incorporate a pre‑incubation step with investigational drug as standard methodology for all transporters prior to the co‑incubation with fresh investigational drug solution and probe substrate. With such standard methodologies, the inclusion of the pre‑incubation step would be anticipated to mask any non-specific binding sites therefore the co‑incubated concentrations of investigational drug would be nominal for IC₅₀ fitting purposes, i.e. IC₅₀ = IC₅₀,_u.

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The top challenges in commercial API manufacturing

Once an investigational medicinal product has demonstrated its safety and efficacy during clinical trials, the next challenge for its Sponsor is to secure a reliable supply chain for its commercial production. Understandably, commercial pharmaceutical manufacturing, including commercial API manufacturing, is a highly regulated area to ensure patients’ ongoing access to a consistently high quality of medicines that guarantees their safety and efficacy. Transitioning from a clinical pilot scale to eventually the larger plant scale required for commercial API production can be a long and arduous process.

The main challenges faced in the journey to commercialization include scaling up production, establishing and maintaining process consistency and reproducibility, and ensuring compliance with regulatory guidelines. In this blog, we explore these areas in detail, discussing how a strategic approach, robust planning, and the incorporation of innovative technologies, can help ensure your drug smoothly and successfully reaches the market.

Developing robust and scalable processes

Increasing the production of active pharmaceutical ingredients (APIs) and drug excipients from milligram to multi-kilogram quantities is arguably the biggest challenge faced in paving the way to reliable commercial pharmaceutical manufacturing. Without the adoption of strategic planning during the process development stage, unexpected issues may arise and lead to significant increases in your program timelines and costs.

Here are our top tips for effectively planning and developing robust and scalable API manufacturing processes, and achieving faster and more seamless scale-up that supports marketing approval and supply security requirements:

• Choice of starting materials – several considerations should be made when selecting the starting materials used in the manufacture of APIs and medicinal products. This includes quality, compatibility, long-term stability, affordability, safety, security of supply, and compliance with pharmaceutical regulatory guidelines. Ensuring your starting materials meet these criteria throughout the entire scale-up process, up to commercial scale, will pave the way for a safe and high-quality medicinal product.
• Choice of equipment and processing technology – where possible, scalable equipment and processing technology should be chosen in API and drug manufacturing. This will help to streamline the technology transfer process, reducing the time and costs required for process optimization and validation, and minimizing the risk of failure. Cost, availability, material compatibility, cleaning processes, and the ability to implement automation and control systems at commercial scale should be considered.
• Process development – the basis of every robust, scalable process is effective API process development. This requires the optimization of several components, including the selection of the synthetic route, and measures for process control and risk management. Scientific and risk-based approaches should be utilized to help gain an in-depth understanding of how any changes to the process during scale-up will affect the safety and quality of the product.
  
  

Read this blog for more detail on how to optimize API process development

Establishing and maintaining process consistency and reproducibility

Establishing and maintaining consistency and reproducibility during scale-up is a complex yet vital task in commercial pharmaceutical manufacturing. Increasing the vessel size and introducing any other necessary changes can drastically alter process performance and product quality. Failure to design and optimize the process to account for these differences could derail production, altering the safety, quality, and efficacy of the API.

The key to ensuring process consistency and reproducibility in commercial scale production lies in strategic planning during process development, with the use of systematic approaches that facilitate the implementation of a robust process control strategy

Implementation of systematic approaches

Several systematic approaches, such as design of experiments (DoE), quality by design (QbD), and process modeling and simulations performed with a software such as Dynochem® are used to improve knowledge of the product and the scale-up process. This includes the thorough impact assessment of any changes in processing parameters on process performance and final product quality.

DoE involves the use of factorial design to plan a series of experiments that test simultaneously variations in individual input factors. The aim of this is to gain an understanding of the individual and combined effects of parameters variability on the process performance and output. Following these experiments, mathematical models, such as response surface methodology (RSM), are used to identify the optimal set of process parameters as well as the acceptable operating envelope for the process.

DoE and process modeling and simulations can be integrated into a QbD approach, which incorporates statistical and analytical methodologies to enhance the process control strategy and build quality and risk management into the manufacturing process. This is achieved by identifying the critical process parameters (CPPs) and critical quality attributes (CQAs) that are associated with the quality, safety, and efficacy of the drug substance and excipients.

Using the QbD methodology, the determined parameters and quality attributes are used to establish a design space. For this, statistical tools are used to explore how combinations of CPPs might interact and impact on CQAs. This helps manufacturers optimize the production of APIs and drug excipients, ensuring the design of reproducible processes that minimize batch failure risks and significantly reduce batch-to-batch variability.

Click here to learn more on how to apply QbD principles to drug development and manufacturing

Adoption of advanced process control systems

The process knowledge gained from experimental modeling approaches, such as QbD and DoE, can be used to develop advanced process control systems. These commonly utilize process analytical technology (PAT), which monitors process parameters on-line in real-time.

When PAT is integrated into advanced process control systems, the analytical data may be used to make automatic system adjustments and maintain process parameters within their predefined limits. This reduces manual intervention and creates a closed-loop control system, allowing for the immediate detection of deviations in process conditions and subsequent feedback control.

By immediately correcting any deviations in the process, advanced process control systems enable real-time release testing, assuring manufacturers that the processes have remained consistent, and that the product meets quality and safety standards. By increasing efficiency and consistency, these systems also help to reduce waste, costs, and product cycle times.

Regulatory compliance

Compliance with current good manufacturing practice (cGMP) regulations is critical to ensuring the production of consistent, high-quality, pharmaceutical products. API manufacturing plants are subject to strict regulations, with complex and ever-evolving requirements, stringent quality standards, and severe consequences in case of non-compliance. To comply with cGMP guidelines, several robust management systems must be in place, including those for data integrity, process control, risk management, and supply chain management:

Data integrity

Consistent, accurate, timely, and complete records are required to provide regulators and stakeholders with the confidence that your medicinal product meets all safety and quality standards. Clarity, consistency, and conciseness of the documents must be maintained across the entire product lifecycle. The development of a robust documentation system can help manufacturers with this, establishing effective procedures for naming, authoring, reviewing, approving, updating, storing, and distributing documents.

Control strategy

There are increasing requirements for a clear, well-defined, and scientifically justified process control strategy in cGMP applications. This should include the selection and evaluation of starting materials, followed by approaches including QbD and DoE to link materials attributes and process parameters to product CQAs. These approaches are used to establish a design space and plan control measures to ensure that CQAs are met and a sound process validation methodology is implemented. Furthermore, the process control and validation strategy should be adapted to the increased production scale throughout all stages of the product’s lifecycle.

Risk management strategy

cGMP guidelines require a systematic approach to risk assessment in pharmaceutical manufacturing. This involves the process of identifying, assessing, controlling and reviewing risks based on their potential for impacting the performance of the process and the quality of the product. Risk management plays a central role in scaling up to commercial production in order to mitigate significant quality risks such as cross-contamination and minimize health and safety risks to operators, especially when handling highly potent APIs. Several tools are available for risk assessment, including failure mode and effects analysis (FMEA).

Supply chain management systems

In the pharmaceutical industry, securing patients’ access to drug supplies post-marketing authorization is a regulator’s number one priority. Regulatory guidelines are designed to cover the entire supply chain, from the supply of raw materials to be introduced in cGMP manufacturing operations through to the manufacturing, packaging, labeling, and distribution of the final product.

Digitalizing the management of manufacturing activities can enhance the visibility and efficiency of inventory management, product monitoring, and data exchange throughout the supply chain. Additionally, supply chain management systems should be designed to incorporate risk management strategies for the prediction, prioritization, and mitigation of risks of product stock-outs. This is not only essential for regulatory compliance and patient access, but it will also increase supply chain efficiency to help overcome issues such as inadequate forecasting, long lead times and build-up of working capital.

How to master commercial pharmaceutical manufacturing

The journey to commercialization can be challenging. Scaling up production while maintaining process consistency, product quality, and regulatory compliance, requires expert process development capabilities, and the adoption of innovative science and risk management methodologies. A common pitfall for the Sponsor of an innovative therapy is to under-estimate the complexity and intricacy of this enterprise, which involves the coordinated optimization of strategies for process control, risk management, data management, and supply chain management.

With ever-evolving regulatory requirements and the increasing urge to shorten drug development timelines, getting your drug to market can seem like a daunting undertaking. That’s why taking some of the pressure off your organization by outsourcing your drug development and manufacturing activities to an expert partner can be the smartest decision. This will ensure your drug is commercialized in the fastest and most cost-efficient way possible, utilizing expertise, facilities, equipment, and processes to anticipate and overcome any challenges thrown at your program with ease.

Evotec offers an integrated end-to-end solution for innovative drug R&D, with the capabilities to support all phases of your drug development program. Your projects are in safe hands with our team of expert scientists who are pioneers in QbD, process design, scale-up, and validation, operating to full cGMP within FDA, MHRA, AIFA and BfArM approved facilities.

Learn more about our API and drug product manufacturing capabilities

Continuous biomanufacturing is reducing the cost of goods of biopharmaceuticals. Achieving continuous manufacturing requires expertise in equipment design.

Download the highlights of Andrea Isby's presentation at Repligen's DSP Workshop in Estonia from May 23rd, 2024 to learn more. 

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High-throughput screening methodologies have accelerated downstream development for monoclonal antibodies by enabling parallelized evaluation of chromatographic resins across a range of conditions. However, scientists must now interpret results in a meaningful and consistent way.

Learn how Just - Evotec Biologics' Downstream Data Browser automates visualization of high-throughput datasets, fits response surface statistical models, standardizes report results from a high-throughput screening method and facilitates comparison across molecules allowing the accelerated development of continuous biomanufacturing processes.

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There is a sigh of relief as the new ICH M12 guideline for drug interaction studies is finally released. The new harmonised guideline, adopted in May 2024, is now likely to be implemented by all the major regulatory agencies who have been actively involved in its creation including the US FDA, the EMA and the Japanese PMDA. Previously, each authority had their individual DDI guidance all with distinct differences in protocols and interpretation, leading to practical challenges in designing and interpreting DDI studies to meet all the recommendations. The new ICH M12 aims to simplify the process by providing a single set of guidelines for the designing, conducting and interpreting metabolic enzyme- or transporter-mediated drug-drug interaction (DDI) studies. The guideline covers both in vitro and clinical DDI studies. It provides a consistent approach to replace existing recommendations from the main regulatory authorities.

The ICH M12 harmonised guideline is concentrated predominantly on small molecules. The DDI of biologics is only briefly covered with a focus on monoclonal antibodies and antibody-drug conjugates. Recommendations on how to address metabolite-mediated interactions and the use of model-based evaluations and DDI predictions are also included.

Watch out for our series of blogs on the key differences between the previous US FDA, EMA and Japanese PMDA guidance and the new ICH M12 and how you might be impacted by the changes. We are also busy updating our popular Everything you need to know about ADME and our DDI guides – we will let you know as soon as these are available!

Read the new ICH M12 guideline on drug interaction studies

We are on hand to assist you with designing, conducting and interpreting your DDI study according to the new ICH M12 guideline:

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With the ASAPprime® tool at Evotec and our dedicated experts, swift data interpretation and precise drug product shelf-life estimation are no longer challenges, but a seamless reality.

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The Twin Screw Wet Granulation (TSWG) is a manufacturing process gaining increased attention in the pharmaceutical industry due to its versatility, scalable nature, and seamless integration into continuous manufacturing lines. Especially advantageous in early pharmaceutical development, where API quantity is limited, TSWG accommodates small batch sizes, facilitating later large-scale campaigns using the same equipment. 
A DoE study was conducted in order to assess the influence of the main process parameters on the characteristics of granules and tablets. A leading formulation containing a soluble drug, namely Niacin, was used, and the factors evaluated in the DoE were the screw design, the screw speed, the Liquid/Solid ratio (L/S), the feed rate and the screen type. 
The responses evaluated were referring to the process (e.g. torque), the granules (e.g. particle size distribution (PSD), density and flowability) and the tablets (e.g. tensile strength, friability and disintegration time). This study provides critical insights into optimizing TSWG processes, ensuring efficient granule and tablet outcomes.

Download the poster, which was presented at AAPS PharmSci360 2023, for comprehensive details on the influential process parameters and their impact on granulation and tablet characteristics in TSWG.

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This text underscores the significance of Orally Dispersible Tablets (ODTs) for enhancing treatment compliance, particularly for patients with swallowing difficulties. Co-processed excipients for fast-disintegrating tablets (CPE-ODT) offer a convenient solution, combining a soluble filler and superdisintegrant. These can be efficiently blended with active ingredients, lubricants, and compressed into tablets, streamlining the development process. Investigating the relationships among compaction stress, compact solid fraction, and mechanical strength is crucial for optimizing tablet composition and speeding up development. Striking a balance between inter-particle bonding strength and porosity is especially vital for ODTs, ensuring rapid disintegration with sufficient mechanical resistance for downstream processes. The study aims to establish a general pre-formulation screening method by generating compressibility, compactibility, and tablettability profiles of CPE-ODTs blended with varying drug amounts. These data offer valuable insights into the impact of drug load on compression behavior and key properties, such as friability and disintegration time, facilitating the efficient development of ODTs.

Download this poster presented at AAPS PharmSci360 2023 for comprehensive details on this formulation screening approach.

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