MEET US AT IMAP 2018
These heterocycles have been appropriately termed pseudoprolines. The pseudoproline approach, originally developed by Manfred Mutter et al. at EPFL Lausanne, usually employed the serine and threonine derivatives, as the oxazolidines were cleaved more readily by acids than the thiazolidines. In the meantime, thiazolidine cleavage conditions suitable for standard Fmoc-SPPS have been developed. The incorporation of cysteine pseudoproline derivatives facilitated the synthesis of cysteine-rich peptides, which will also benefit from milder deprotection conditions. The acid lability of both types of heterocycle can be fine-tuned by the choice of substituents at the 2-position. The isopropylidene group of the oxazolidine (X = O, R’, R’’ = CH3) proved to be the best choice for Fmoc-SPPS, as the cycle is opened readily yielding serine (R = H) or threonine (R = CH3) during the final cleavage with TFA. Both isopropylidene (X = S, R’, R’’ = CH3) and benzylidene (X = S, R’ = 2,4-Dimethoxyphenyl, R’’ = H) derivatives have been used for incorporating cysteine, as the rings can be cleaved by TFA. The main drawback of this approach is the difficult coupling of the following amino acid to the hindered heterocycle. To circumvent this, Fmoc pseudoproline dipeptides were introduced (Fig. 2).
The coupling of these building blocks represents the most straightforward method to incorporate pseudoprolines The «preventive» insertion of such moieties is highly recommended when synthesizing long peptides lacking prolines (Fig. 3).
The repeated inclusion of pseudoproline units during the elongation of the peptide will improve the overall coupling efficiency, even if aggregation does not pose a severe problem. A considerable number of syntheses of long or «inaccessible» peptides, which succeeded only due to the insertion of pseudoprolines in appropriate positions, has been published since the introduction of these derivatives. Difficult peptides as IAPP or RANTES could be synthesized following standard Fmoc-SPPS protocols after evaluation of the required number and optimal position of the oxazolidine moieties to be inserted. As with Fmoc amino acid derivatives, couplings can be accelerated by microwave irradiation. Pseudoproline dipeptides show their high versatility as building blocks not only during the SPPS of long and difficult peptides. When synthesizing short peptides, a distinctly purer crude product may be obtained by incorporating merely a single pseudoproline unit. The heterocycles are left intact when cleaving fully protected peptide fragments from SASRIN or 2-chlorotrityl resin with diluted TFA and their presence markedly increases the solubility of the cleavage products. Accordingly, the purification and coupling of the fragments as well as the modification of partially protected peptides in solution are facilitated by insertion of pseudoproline moieties.
As fragments containing a C-terminal proline, fragments with a C-terminal pseudoproline can be coupled with minimal concomitant racemization. Hence pseudoproline dipeptides may establish additional possibilities in convergent peptide synthesis. The incorporation of an oxazolidine moiety greatly facilitates cyclizations of serine- or threonine-containing peptides, disulfide bridge formations, as well as head-to-tail cyclizations. The increased tendency to cyclize is due to the presence of a temporary cis-amide bond in the molecule.
Even though cysteine occurs only rarely in peptides and proteins, far less often than serine and threonine, the incorporation of cysteine pseudoproline is highly attractive due to the peculiar properties of the amino acid. Postma and Albericio showed that on-resin macrocyclization of cysteine-containing peptides proceeds more smoothly if Cys(Trt) is replaced by a cysteine pseudoproline. They also observed that cysteine 2,2-dimethylthiazolidines are opened during the final cleavage with TFA/TIS/H2O (95:2.5:2.5), cleavage duration and temperature depend on the nature of the subsequent amino acid. As their oxazolidine counterparts, cysteine pseudoprolines facilitate end-to end cyclization. The option of desulfurizing the amino acid may add alanine to the choice of amino acids allowing insertion of a pseudoproline dipeptide. Cysteine derivatives are notorious for their propensity for racemization when activated. Use of the appropriate thiazolidine dipeptides allows avoiding this risk, an advantage worth considering when synthesizing multiple disulfide bridge-containing peptides or anchoring cysteine to carriers.
If its thiazolidine ring is left intact, 2,2-dimethylthiazolidin-4-carboxylic acid (Me2Thz), conveniently introduced as pseudoproline dipeptide, acts as a highly effective cis-proline mimic. An analog of the cyclopeptide phakellistatin 19 containing three Me2Thz residues replacing proline, all of them incorporated by coupling pseudoproline dipeptides, showed enhanced activity. For synthesizing peptides containing this proline surrogate the standard TFA-labile lateral protecting groups may have to be replaced by highly acid-labile moieties such as Mtt (His), Trt (Ser) or OPp (Glu).
A major disadvantage of the pseudoproline approach cannot be left unmentioned: The solubilizing effect of the oxazolidine moiety is lost when deblocking the peptide with TFA. Albeit the quality of the crude product may be vastly improved, further purification will be tedious due to its low solubility.
The N-O shift, a notorious side reaction during HF cleavage, turned out to be the key to the solution of this dilemma. This acid-catalyzed rearrangement involving the hydroxyl moiety of serine or threonine residues can be smoothly reversed by keeping the peptide in a slightly basic medium (Fig. 4).
As the N-O shift, i.e. O-isoacyl peptide formation, causes a disruption of the secondary structure, it is accompanied by an increase in solubility. At first, this type of depsipeptide was obtained by Fmoc-SPPS involving on-resin esterification of the subsequent Fmoc-amino acid to the free hydroxyl moiety of an N-terminal Boc-serine/threonine followed by elongation of the peptide. Low conversions, concomitant racemization, and diketopiperazine formation during the subsequent SPPS cycle are the main drawbacks of this straightforward approach. The recently introduced Fmoc O-acyl dipeptides help to overcome these problems (Fig. 5), though diketopiperazine formation is not affected by the method chosen for introducing the ester bond (incorporation of more base-labile Bsmoc Nα-protection at this position helps to reduce the extent of this side-reaction).
As pseudoproline dipeptides, O-acyl dipeptides turned out to be versatile building blocks for the synthesis of difficult peptides such as β-amyloid (1-42), which indeed showed an improved solubility and reduced propensity for fibril formation, and insulin, where isoacyl dipeptides were inserted in both A- and B-chain. Peptides containing O-acyl bonds to serine or threonine may act as soluble prodrugs, hence they have also been termed «switch peptides» (Fig. 6).
β-elimination of O-acyl dipeptides during activation has been described by Coin et al. If this side reaction poses a problem pseudoproline dipeptides should be incorporated in place of the isoacyl dipeptide to disrupt the aggregated structure.
The quality of a material produced by a manufacturing process depends entirely on the process itself, and the robustness built into that process during the development lifecycle. For a number of years, many companies have been looking towards the Quality by Design (QbD) approach to process development to ensure this robustness, and Bachem is no exception. Bachem, the world’s longest established and largest manufacturer of Peptide API, prides itself on the quality and consistency of peptide API produced for customers. One of the lynchpins of this success has been derived from FDA guidance on QbD approaches and Quality Risk Management.
The International Committee for Harmonization (ICH) defines QbD as follows:
«A systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and control, based on sound science and quality risk management.» This provides a framework for us as a contract manufacturer, in collaboration with customers, to gain product and process understanding for the manufacture of Peptide API with clear goals having been defined:
- Gain as deep understanding as possible of the process starting at the earliest possible stage
- Partner with customers at every point during development (intended use drives the quality requirements of the final product)
- Gain full understanding of relationship between every stage of the process and the final product
- Finalize a commercial process delivering the best quality of material for the intended use
These goals can be achieved, in principle, by a forward-thinking approach, commencing with an ongoing systematic review of the entire manufacturing process, and all elements therein. Such a review allows the proactive identification of emerging and/or potential risks to every step of the process based on the development stage, the experience gained so far of that specific process as well as prior knowledge of similar processes. Once risks have been identified, there follows the possibility to mitigate those risks, again in a proactive manner, to end up with the best process for the material required.
Further to this, ICH guideline Q11 «Development and Manufacture of Drug Substances» recommends an enhanced approach to manufacturing process development incorporating elements such as prior knowledge and targeted experimentation to identify material attributes and process parameters that could affect drug substance Critical Quality Attributes (CQAs). Using these principles, the functional links connecting material attributes and process parameters to CQAs can be determined. This enhanced approach, combined with ICH guideline Q9 «Quality Risk Management», an appropriate control strategy can be established for the manufacturing process for later phase clinical trials, process validation and filing.
During process development, a control strategy is required to ensure that potential risks to the manufacturing process, and indeed the CQAs of the material emerging from that process, are minimized to an acceptable level for process validation and filing. Failure to establish such a control strategy can have profound impact on the quality of the material, leading to potential batch failures and in the worst cases even compromising patient safety. Introduction of a Quality Risk Management system at the appropriate time during development ensures that risks are identified and minimized, and such events do not take place.
A sensible control strategy is defined by the characterization of the manufacturing process. Process characterization is performed as a multi-step activity which comprises risk assessment, followed by work-packages and experiments designed to allow mitigation of risks emerging from the assessment. This iterative process is focused on Process Validation (PV, otherwise known as Process Performance Qualification or PPQ) readiness, as once the pre-PPQ manufacturing campaign has started, the process is effectively «locked» in its final form.
Process characterization, as stated in the previous paragraph, requires a risk assessment and mitigation process. The first step of this is naturally identification of process risks. There are a number of ways of approaching the identification and assessment of risks in a manufacturing process. Bachem has previously explored a number of these and has selected the concept of FMEA or «Failure Modes and Effects Analysis» as the method of choice.
FMEA is defined as «a systematic procedure for the analysis of a process or system to identify the potential failure modes, before they occur, and their potential causes and effects on that system performance». The manufacture of peptides falls under «process» in this definition, and hence the principles of FMEA can be applied as a systematic analysis of the manufacturing of a peptide API to identify risks, or failure modes. Once this has taken place, they can be characterized as far as possible, and steps can be taken to mitigate those risks or failure modes should they be considered potentially damaging to the ability of the process to produce peptide API of the appropriate quality. In an ideal world, FMEA would identify every risk, and work-packages could be developed to mitigate those risks entirely. The real world dictates a more pragmatic approach, and so the goal falls more into the realms of evaluating the available process knowledge (depending on the manufacturing experience and clinical phase). From there, a list of manufacturing risks can be generated, and those risks can be prioritized according to impact on Critical Process Parameters (CPPs) and therefore CQAs. The final stage is to use this list to guide the efforts and resources to focus on priorities for work-packages and other activities for further process characterization activities to bring the manufacturing process closer to validation (or PPQ) readiness.
Figure 1 Quality risk management (ICH Q9) – risks must first be identified and assessed, following which a control strategy can be developed to reduce risks to an acceptable level for process validation and filing.
FMEA as a concept drives the understanding of risks and their impacts. The term «Failure Modes» itself holds as an inherent meaning the concept of risks, or «what could go wrong». The «Effects» naturally refers to the impact of something going wrong, and «Analysis» attempts to define or quantify the likelihood and potential impact of an adverse event taking place during manufacturing. In the context of a peptide manufacturing process, the failure modes or risks have potential impact on CQAs of the API material emerging from the process, and thus have potential to impact patient safety, hence a robust methodology is needed to identify such risks.
Bachem applies FMEA by undertaking a systematic review of the entire manufacturing process, looking for risks or failure modes at every single step. Synthetic peptide manufacture typically follows 4 stages:
- Solid phase peptide synthesis (peptide chain built on a solid support)
- Cleavage of the peptide chain from the solid support
- Purification of the peptide chain
- Isolation of the final API (usually by lyophilization).
Each stage requires comprehensive Batch Production Records in order to create a repeatable process. Together the 4 sets of batch documentation provide a line-by-line description of the overall manufacturing from start to finish. The Batch Production Record templates (known within Bachem as Master Batch Production Records or MBPRs) provide the basis for the FMEA.
The first stage of the FMEA is familiarization with the process as it exists. As suggested in the previous paragraph, this depends heavily on the MBPRs, as well as any other prior knowledge that may exist, such as process development reports, as well as knowledge held by the manufacturer. This, and the rest of the FMEA process, is carried out as a team effort, with all technical, quality and analytical aspects of manufacturing being represented. It is at this stage that subject matter experts are also drawn into the workflow, as the insight they offer is invaluable.
The next stage is to set up the FMEA into a meaningful medium for the risk assessment proper. Bachem performs this by setting up a table in Excel, listing every process parameter as a separate item. In essence this means that every line in a MBPR is listed as a separate line in the table. Following this comes the population of the table. Each line is populated with data collected from numerous sources, including the executed batch production records from previous manufactures, process development reports and other batch data and know-how. Once the template table has been completed, the risk assessment itself can progress. It should be noted that once the risk assessment is completed, the outcome and relevant information are compiled in a report.
Risk of an adverse event taking place on a single process parameter, or process step, depends on a knowledge of the operating ranges of that parameter. In general, a «set point» or ideal operating point is required, around which appropriate operating ranges can be configured. The characterized range, as the name suggests, is the operating range of parameters that have been actively measured during process development or through prior knowledge. The proven acceptable range also depends on prior knowledge or direct measurement, and is the range of operation within which the risk to CPPs or CQAs have been proven to be acceptable. Finally, the normal operating range (generally much tighter than the proven acceptable range) which defines the optimal working range of a parameter designed to allow minimal risk of impact on CPPs and CQAs. As part of Bachem’s approach to minimizing risk, normal operating range is usually defined as a calculated proportion of proven acceptable range (see Fig. 2).
Figure 2 Parameters established and used in FMEA.
The assessment of risks for each process parameter should be systematic and meaningful, as well as objective and standardized. Bachem has developed a risk rating system that allows such a standardized approach, while incorporating the element of risk identification (the «what could go wrong»). A score is assigned to each of three elements based on the outcome of the assessment. Those elements are severity of effect (or excursion) on the final drug substance (or CQA), the likelihood of occurrence of the effect (or excursion) and finally the probability of detection of an excursion should it have occurred. The framework for assigning scores to these elements is summarized in Table 1.
|Score||Severity (S) of effects on final DS||Likelihood of Occurence O)||Probability of Detection (D) (IPCs and release testing)|
The scoring for each category is assigned as a team effort, incorporating the input from subject matter experts, based on the concepts of the operating ranges as described earlier in this article. It should be noted that the team effort generally incorporates input and/or comprehensive discussions with our clients, as desired. The output is designed to be an objective risk assessment that provides a meaningful measure of the impact of operating range excursions, constrained by the pragmatic measures of likelihood of an excursion and the possibilities to detect such an excursion should it exist.
Combining the 3 scores, it is possible to define a risk classification. Bachem follows a system of assigning a Risk Prioritization Number (RPN), which is based on a simple calculation of:
RPN = Severity x Occurrence x Detection
The RPN can then be used to drive decisions around the mitigation strategies for any individual parameter (process step, or line of the MBPR).
Bachem takes a conservative approach to the assignment of RPNs, in this context, to provide a manufacturing process that is as stable, robust and risk-free as possible. The scoring system is summarized in Table 2.
Table 2 Risk classification and mitigation measures according to Risk Prioritization Number (RPN). Colours used in this table are those used in the FMEA document itself for clarity and ease of interpretation.
For a complete mitigation-free approach, the scoring is very low. The scoring range where further investigation or active mitigation is required covers more than 90% of the scoring range. The outcome of this is that there is almost a statistical guarantee that each process step will be actively assessed based on risk. The scoring categories are labelled in the Excel FMEA table with colors (as shown in Table 2) which also makes a straightforward overview of process steps where mitigation measures are recommended or required.
The final part of the overall FMEA process is the mitigation stage – after all, identification of risk is merely an academic exercise without the ability to use the information gained. Risk mitigations, knowledge gaps and process improvements can be actively pursued based on the outcomes of the FMEA. The characterization score can be used to focus the resources based on the severity of the risk, or the ability to detect the outcome of that risk.
Risk mitigation and plugging knowledge gaps generally requires experimentation, or at the very least a reliance on prior knowledge. Process Characterization is a series of experiments, generally emerging from the output of the FMEA, which varies operational process parameters in a systematic way in order to determine the impacts of such excursions on drug substance quality (CQA) and process performance parameters (CPPs). Bachem, as a CMO, relies heavily on the input and experience of customers in the compilation of process characterization experiments, and indeed this part of the risk mitigation is performed in close collaboration with our clients. The process characterization work is split into work-packages of a defined scope, designed to address specific risks emerging from the FMEA assessment. The outcomes of the work-packages provide the confidence that the process is de-risked and suitable to proceed to process validation. Data generated from work-packages are documented in reports to benefit the current process, but also potential future processes, and also to provide a customer a comprehensive overview of the risk mitigation undertaken.
Examples of risk mitigation specifically related to peptide manufacture are of course many and varied, however a few are captured below:
Stage 1 (SPPS)
Influence of starting materials on final impurity profile
Stage 2 (TFA cleavage)
Hold times and temperatures for intermediate solutions
Stage 3 (Purification)
Influence of residual solvents (e.g. IPE, ACN) on purification (typically preparative HPLC)
Stage 4 (Ion exchange, final isolation of DS)
Impact of e.g. reduction of free AcOH in freeze drying step
Control over water uptake of DS during final packaging
Lyophilization cycle and maximum batch scale
In summary, the FMEA approach to risk assessment and mitigation is a proven method of risk identification, assessment and characterization, based on the QbD approach. These are enshrined in ICH guidelines Q8, Q9 and Q11, which exist only to push API manufacturing processes towards greater robustness, and material quality suitable for the intended application. The FMEA itself provides a deep and comprehensive overview of risks at every step of the process, and allows work-packages to be constructed (with input from subject matter experts and customers) that eliminate or constrain those risks. Risk assessment is essentially a mandatory part of an API development lifecycle, and the FMEA approach provides a systematic approach, with the intention of proceeding to a successful process validation, launch and commercial supply of API.
FDA Guidance for Industry «Process Validation: General Principles and Practices (R1)»
ICH Harmonised Tripartite Guideline «Pharmaceutical Development Q8 (R2)»
ICH Harmonised Tripartite Guideline «Quality Risk Management Q9»
ICH Harmonised Tripartite Guideline «Development and Manufacture of Drug Substances (Chemical Entities and Biotechnological/Biological Entities) Q11»
What is your official job title at Bachem?
Group Leader Purification, Bachem UK
How long have you been with Bachem?
I started working for Peninsula Labs Europe in November 1989, which was acquired by Bachem in 1999. Therefore, I have been here for 28 years!
Briefly, what do you do at Bachem?
As Group Leader of Purification, I am responsible for the Purification Department at BUK. As a group, we purify over a hundred different synthetic peptides every month.
What do you like to do outside of work?
I like spending time outside, anything from gardening, walking, cycling, fishing, skiing etc.
What makes a perfect day for you?
Strolling along a beach in Barbados.
What do you like most about your job?
Having purified peptides for 28 years, the best thing about my job is the constant change. Technology is constantly improving, allowing for the manufacture of increasingly difficult and complex peptides.
What do you do for fun?
I enjoy travelling a lot, especially for European city breaks. I also enjoy finding and drinking different craft beers. Sometimes, I am able to enjoy both of these at the same time.
What is your preferred peptide?
I never would have thought I would say this, but Amyloid β-Peptides. They are easily the most difficult group of peptides that we have tried to produce. But through many incremental improvements in synthesis, cleavage and purification, we can know manufacture many analogues for Alzheimer’s research.
Thank you very much Neil.
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