Developments in stability testing and evaluation

In-use stability: the BSAC antimicrobial drug stability programme; an innovative model for collaborative data sharing

Dr Conor Jamieson, Pharmacy Team Leader – Antimicrobial Therapy, Sandwell and West Birmingham NHS Trust

Dr Jamieson explained the role of outpatient parenteral antimicrobial therapy (OPAT) in the management of infection. OPAT is a model of treatment that can facilitate early discharge from hospital or admission avoidance, reducing pressure on the NHS and improving the experience for patients who might otherwise need a long hospital admission.

The challenges of OPAT are finding antimicrobial agents with suitable characteristics for once- or twice-daily administration, which are convenient to administer, safe and meet the needs of the service as well as the antimicrobial stewardship agenda. He contrasted the treatment of Staphylococcus aureus infection using either ceftriaxone or flucloxacillin, to demonstrate the challenges posed by once-daily dosing with ceftriaxone versus the narrow spectrum, preferred agent, flucloxacillin, which required multiple daily dosing.

Administering antibiotics as continuous infusion via an elastomeric device is also a useful option for OPAT, but there must be assurance that the drugs are stable over the 24-hour infusion period. There were no data meeting the NHS guidance on stability available for key antibiotics for OPAT, so the drug stability-testing programme was launched to make data available for open access by the OPAT community.

Dr Jamieson then discussed the findings for flucloxacillin stability in citrate buffer in two commercially available, ambulatory devices; INfusor LV (Baxter) and Accufuser (Woo Young Medical). Initial stability evaluation found that the product was too unstable in WFI, 0.9 percent NaCl, MCP buffers, 5.25 percent citrate buffer (buffer pH ranges were pH 5-8); showing both high degradation and precipitation. Therefore, 0.3 percent citrate buffer was chosen at two clinically‑relevant concentrations, which were stable for 24 hours when heated to 37°C. In addition, stability in the two elastomeric devices at two different concentrations was evaluated. In both cases good stability data were generated. A similar study was conducted with meropenem, but stability for only six hours could be demonstrated, limiting its usefulness in OPAT. Work had been due to commence on ceftazidime, but significant challenges with its stability and the formation of the neurotoxic degradant pyridine were recognised. Future projects include ceftazidime/avibactam and possibly benzyl penicillin, amoxicillin, cefepime and temocillin. The working party is keen to work in partnership with the pharmaceutical industry and device manufacturers to generate and disseminate open access stability data.

Stability prediction modelling – statistical considerations

Dr Jonathan Bright, AstraZeneca

The time until a drug product stored at 25°C/60%RH breaches an impurity limit is often shelf-life limiting; you can either place the sample on real-time stability and wait, or use six or so harsher combinations of T and RH before using statistical modelling to predict what will happen at 25°C/60%RH. A key question is: for any given T, RH and timepoint, how many replicates are required?

Dr Bright then reviewed several different statistical designs but surprisingly demonstrated that the outputs were often very similar. He concluded that the experiment is usually designed to efficiently estimate the relation between the measured response of interest and the explanatory variables. However, if the purpose of the experiment is not the relationship per se, but a prediction outside of the design space, then other – perhaps surprising – designs may be equally good alternatives. Dr Bright then reviewed some typical data outputs. He concluded that it is important to “picture the raw data” and to reflect on the importance of the zero time value – its re‑use and high leverage – by using repeats and then estimating a single zero time value that is applicable to all conditions. He concluded that it was also necessary to “picture the data” being modelled against the fitted model. Be aware that model diagnostics do not speak directly to the quality of predictions outside the design space and extrapolations should be minimised as far as possible.

Finally, Dr Bright concluded that if the purpose of an experiment is a prediction outside the design space, you must be aware that some unusual-looking designs may be good options. It was necessary to “picture both the raw data and the modelled data” (if different). The zero time value is critical – it should be based on several measurements and may be estimated as a single value applicable to all conditions. Good model diagnostics (R-squared etc) are necessary, but not sufficient, for a good prediction.

Risk-based predictive stability – an industry perspective

Rachel Orr, GlaxoSmithKline

Rachel Orr discussed how utilising a ‘risk-based approach’ is common practise within the pharmaceutical industry and gave examples of it; such as the modelling of process parameters to develop design spaces, quality by design (QbD) and technical risk assessments, sunset testing and animal testing models to name a few. In addition, Orr noted that risk-based approaches are encouraged by regulators, citing numerous publications from the FDA, WHO, EMA and ICH to demonstrate this. It was, however, acknowledged that taking risk-based approaches in stability has lagged behind other areas of the industry and that this area needs considerable improvement.

Risk-based stability can be subdivided into two approaches: lean stability, eg, testing, bracketing and matrixing, or predictive stability (RBPS), such as stress testing and accelerated stability monitoring. Orr commented that there has been an evolution in the stability of science since ICH Q1A was first published with improvements to both modelling tools and protocol development. However, due to a lack of updated guidance, RBPS has been implemented inconsistently across the industry. In recognition of this, in 2015 the IQ consortium launched a working group to focus on RBPS tools to optimise pharmaceutical development. The working group has approximately 50 members from 18 companies across the pharmaceutical industry and 18 pharma companies were surveyed in 2016. The results were first published in 2017. The key findings of this survey were that although RBPS was regularly used in regulatory submissions by most of the surveyed companies (70 percent), this was primarily in clinical phases (80 percent) and the majority of companies (85 percent) continue to generate full ICH stability data despite several companies having successfully leveraged these advanced stability approaches within their development paradigm.

The FDA guidance for post-approval changes subdivides changes into ‘minor’, ‘moderate’ and ‘major’, based on what has been changed and the perceived risk that this introduces. From a stability perspective, these categories are allocated on the following basis: (i) minor – no stability data is required, (ii) moderate – some additional stability is required as supportive data and (iii) major – a complete stability dataset will be required post change. However, in place of this generic classification for changes, the option to use RBPS tools to assess the impact of the change for appropriate categorisation was proposed. Orr discussed a decision tree, which would be useful in such discussions, where the two key questions would be: (i) are there any changes to site, route, process or packaging before file? and (ii) does modelling suggest any change in stability performance?

Orr concluded by indicating that risk-based approaches are actively encouraged across various areas of pharmaceutical development. Unfortunately, stability has lagged behind other areas with respect to taking up these opportunities. The IQ consortium has lobbied companies in an attempt to harmonise approaches and initial scoping of the landscape has demonstrated a lack of confidence in subsequent regulatory acceptance. Consequently, the group has defined some proposed regulatory templates for future use. However, continued advocacy with regulators is still required.

Stability management regulations: global stability requirements – development of a CMC regulatory intelligence solution

John Cleverley, Clarivate Analytics

John Cleverley indicated that one of the major hurdles faced when launching a new drug is the approval of chemistry, manufacturing and controls (CMC) data. CMC modules are uniquely challenging because they must be compiled for individual countries and they depend on regulatory requirements that can be difficult to find and verify. In particular, access to low- and middle-income countries (LMICs) is negatively impacted by diverse regulatory requirements and limited CMC requirement visibility. Across multiple countries each requirement can result in a cumulative impact on costs and delays in product introduction; a global health product is typically introduced in over 30 countries. CMC issues include:

• some countries have specialised excipient requirements; eg, restrictions on porcine products
• some Latin American, African and ASEAN (Association of South East Asian Nations) countries require 30ºC/75%RH stability studies, while most will accept 30ºC/65%RH.

A successful stability study will establish the shelf life date of the drug product for the specified climatic zone under the product label-stated storage conditions. However, even where the US and EU have incorporated most ICH guidelines, requirement differences remain. Whereas EMA stability requirements are aligned with ICH guidelines, in the US the required number of batches and storage time depend on product dosage form and stability.

Clarivate Analytics has developed Cortellis CMC Intelligence, with the support of a grant from the Bill & Melinda Gates Foundation, with the intention of establishing a long-term, sustainable database of CMC regulatory requirements for small molecule products, with an initial focus on LMICs.

Cleverley went on to explain that Cortellis CMC Intelligence provides information on the following stability parameters: number and type of batches required, stability commitment, acceptance of bracketing/ matrixing, need for in-use stability data, whether the country follows ICH, whether statistical extrapolation is acceptable and whether stress testing and photostability are required.

In summary, CMC requirements are challenging because they must be compiled for individual countries; national regulatory authorities require stability data to ensure a product maintains consistent quality and shelf life and there is a need to manage stability requirements across multiple territories and address variations in requirements.