Article pubs.acs.org/OPRD
Development of a Control Strategy for a Final Intermediate to Enable Impurities Control Antonio Ramirez,* Daniel M. Hallow,† Michael̈ D. B. Fenster, Sha Lou, Nathan R. Domagalski, Srinivas Tummala, Sushil Srivastava, and Lindsay A. Hobson*,‡ Chemical and Synthetic Development, Bristol-Myers Squibb Company, One Squibb Drive, New Brunswick, New Jersey 08903, United States S Supporting Information *
ABSTRACT: This manuscript describes the development of a control strategy for impurities in the final intermediate step of the asunaprevir drug substance utilizing the concepts outlined in the International Conference on Harmonisation guidelines (ICH Q8 (R2), Q9, Q10, and Q11). Detailed mechanistic understanding enabled the construction of a kinetic model that was used in conjunction with a process risk assessment and well-defined quality attributes to guide the development of the reaction design space. Implementation of continuous monitoring of the reaction facilitated the expansion of the design space and provided suitable parameter ranges to enable a robust process for commercial manufacturing.
1. INTRODUCTION The term “control strategy” was coined by the International Conference on Harmonisation (ICH) guidances with the end objective of assuring process performance and product quality.1 A control strategy is composed of a set of controls derived from current product and process understanding that can include parameters, attributes related to drug substance, drug product materials, components, facility and equipment operating conditions, in-process controls, finished product specifications, and the associated methods with frequency of monitoring and control. Control strategies for drug substance development that focus on the management of impurities generated during its chemical synthesis have been implemented and described in the literature.2 Common to these reports is the inherent challenge offered by the multistep assembly of a drug substance because each step must be developed with the upmost rigor to ensure process performance and product quality. As a result, multiple control strategies must be put into practice, and an overall control strategy that encompasses the individual strategies from starting materials to drug substance becomes essential. The key elements of a control strategy following a Quality Risk Management approach are provided below: • Identification of quality attributes (QAs) and critical quality attributes (CQAs) for starting materials, intermediates, and drug substances; • Establishing acceptance criteria for input materials, reagents, and solvents; • Determining process parameter criticality and ranges; • Understanding the fate and purge of impurities; • Defining the in-process controls; and • Developing specifications. Development of the control strategy is an iterative process used to ensure quality throughout the lifecycle of a product. If a change in the process is required either in development or continuous improvement, the control strategy should be re© XXXX American Chemical Society
evaluated to ensure that the quality of the drug substance is maintained. In general, development of a control strategy begins with identification of drug substance CQAs, determination of the QAs of the corresponding intermediate and starting materials, and a risk assessment of the variables that could impact the quality of the drug substance, e.g., input material attributes and process parameters (critical and noncritical). These activities rely on an adequate understanding of the overall process and underlying reaction mechanisms and preface experimental studies toward the selection of parameter ranges to ensure consistent quality in concurrence with the quality target product profile (QTPP). The selected ranges can be described by proven acceptable ranges (PARs)3 built upon univariate experimentation or can be presented in terms of a design space that correlates ranges of material attributes with process parameter ranges derived from multivariate experimentation.4 PARs or design space constitute key elements of the control strategy. Development of a control strategy for the final intermediate during preparation of the asunaprevir drug substance5 is described herein. Designation of the final intermediate as the “quality gate” intermediate6 of the process introduces distinctive challenges that influence the entire strategy. Our approach to control critical impurities is rooted in a detailed understanding of the reaction mechanism as well as the purge capability during workup and crystallization. The discussion begins with an overview of the process, followed by determination of quality attributes and a process risk assessment. Next, the mechanistic studies of the reactions involved in the generation of the final intermediate, consumption of the corresponding input materials, and impurity formation are described. This knowledge enabled the construction of a mechanistic model for guidance in the selection of a design space and helped to identify the parameters Received: July 29, 2016
A
DOI: 10.1021/acs.oprd.6b00253 Org. Process Res. Dev. XXXX, XXX, XXX−XXX
Organic Process Research & Development
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Scheme 1. Synthesis of the Final Intermediate
that could impact the impurity profile of the active pharmaceutical ingredient (API).
Table 1. Collective Risk Assessment: Identification of Quality Attributes drug substance CQAs
2. PROCESS OVERVIEW: PREPARATION OF THE FINAL INTERMEDIATE Preparation of the final intermediate, summarized in Scheme 1, involves a nucleophilic aromatic substitution reaction between the triple salt of an acidic starting material (compound 1) and an electrophile (input 1). The individual steps entail (a) deprotonation of input 2 with potassium tert-butoxide (tBuOK) to form tripotassium salt compound 1, (b) coupling of compound 1 with input 1 to form compound 2 as its salt, and (c) aqueous quench of the reaction upon completion. Following workup, crystallization, isolation, and drying, the final intermediate is obtained. It is noteworthy that, throughout the coupling step, the reaction mixture forms a slurry that contains the potassium salts of compounds 1 and 2.
input 1
input 2
assay
DS
outcome
√
√
develop parametric control and specifications route of synthesis consistently assures identity; develop orthogonal methods for identity develop control of critical impurities, including genotoxic impurities and residual solvents control of batch to batch variation define control of particle size and form
identity
√
√
√
√
impurities
√
√
√
√
noncritical QAs particle size and form
3. DEFINING THE QUALITY ATTRIBUTES OF THE FINAL INTERMEDIATE The QAs of the final intermediate were identified through a collective risk assessment of the critical quality attributes of the drug substance, which were established based on the quality target product profile (QTPP). Completion of the risk assessment was enabled by prior knowledge and development work. The quality attributes of the final intermediate were identified as assay, identity, and related substances (impurities) (Table 1).
final intermediate
√
understanding, the risk assessment identified the parameters listed in Table 2. The effect of these parameters on the quality attributes of the final intermediate was evaluated based on prior experimental knowledge and DoE, and the risk was assessed to conclude that four parameters in the coupling reaction, including deprotonation hold time, coupling reaction temperature, excess equivalents of base, and equivalents of residual water, had the highest impact on the formation of the critical impurities. Six process-related impurities were considered during the development of the control strategy (i.e., impurities A−F, vide infra). Of the six impurities, two were identified as critical to quality and yield (impurities A and B) as the levels of the other impurities remain within the purging capability of the downstream process (see the Supporting Information for additional purging information).8 The simplified Ishikawa diagram shown in Figure 1 depicts the final intermediate step with regard to the QAs of the final intermediate.
4. PROCESS RISK ASSESSMENT An initial qualitative risk assessment was conducted to identify all reaction process parameters (including material attributes) that might impact the quality attributes and yield of the final intermediate. Drawing on knowledge gained through process development together with scale-up data and mechanistic B
DOI: 10.1021/acs.oprd.6b00253 Org. Process Res. Dev. XXXX, XXX, XXX−XXX
Organic Process Research & Development
Article
Scheme 1, the final intermediate is prepared through a coupling reaction between input 1 (the electrophile) and compound 1 (the nucleophilic tripotassium salt of input 2). Compound 1 is generated from input 2 by the addition of >3 equiv of t-BuOK. The deprotonation is complete and virtually instantaneous at 50 °C as shown by IR spectroscopic and kinetic studies. Consequently, the model approximates the initial concentration of compound 1 with the concentration of its precursor input 2. Reactions leading to the formation of impurities A−F and associated elementary steps considered in the model are shown in Scheme 2 and Table 3, respectively. Impurity A is generated by addition of unreacted compound 1 to a transient species derived from compound 2 which is in equilibrium with impurity A, (eq 1, Scheme 2).10 The concentration of impurity A depends on the ratio of compound 2 to t-BuOK because both nucleophiles compete for the transient intermediate. In agreement with this observation, the reaction that forms impurity A displays an inverse-order dependence in tBuOK. Impurity B is formed via two pathways (fast hydrolysis of input 1 (eq 2) and slow degradation of the desired product through elimination), which also results in the formation of impurity C (eq 3). Consequently, the growth of impurity B shows a bimodal kinetic profile with a rapid onset (hydrolysis of input 1) followed by a slow progression (elimination of compound 2). Controlling the charge of base is crucial: low excess base affords higher levels of impurity A, whereas high excess base can lead to greater decomposition of compound 2 by elimination, resulting in higher levels of impurity B. Although impurity A forms to a lesser extent than impurity B, it was considered critical to quality in the final intermediate because it converts to an impurity that may impact the quality of the drug substance. Impurity B could be purged at relatively high levels; however, its formation via decomposition of both starting material and product would lead to lower yields. Impurities D and E correspond to degradation products of compound 1 caused by a base-mediated deprotection of the carbamate group analogous to that observed for compound 2 (eq 4). In this case, the direct kinetic correlation between KOH concentrations and carbamate deprotection rates as well as the detection of carbon dioxide by IR spectroscopy provide support for a mechanism involving the addition of KOH to the transient species followed by instantaneous decarboxylation to afford impurity D. Impurity E could also form from decarboxylation of compound 2, however, because residual water is consumed at the start of reaction, only the pathway shown in eq 4 was considered in the model. Impurity F results from reduction of input 1 in the presence of excess base via a free radical mechanism with solvent participation as indicated by kinetic and labeling experiments (eq
Table 2. Assessment of Coupling Reaction Process Parameters That May Affect Impurity Levels variable deprotonation reaction hold time temperature excess equivalents of base equivalents of residual water equivalents of input 2 concentration ramp time7 a
potential impact and impurities formeda formation of impurities A and E reaction kinetics and formation of impurities A, B, C, and E reaction kinetics and formation of impurities A, B, and C reaction kinetics and formation of impurities B, D, and E reaction kinetics and formation of impurities A, B, and C reaction kinetics and formation of impurities A, B, and C reaction kinetics and formation of impurity B
Impurity formation and structures are provided in Scheme 2.
On the basis of the potential impact to drug substance quality and yield, a control strategy was established that comprised the following elements: (a) process parameters to limit impurity formation, (b) acceptance criteria in the final intermediate, (c) specifications for reagents, solvents, and starting materials, (d) development of appropriate analytical methods to support the process, and (e) in-process controls to ensure impurity purging.
5. DEVELOPMENT OF A MECHANISTIC MODEL A mechanistic model for the coupling reaction was built to determine process conditions that would facilitate the control of impurities A and B within acceptable limits while ensuring quality (impurity A), maximizing reaction yields (impurity B), and maintaining the other impurities within their purging capability. The model was built with the purpose of guiding the selection of an invariant design space defined by specified ranges. Furthermore, it would provide knowledge about potential edges of failure and thereby enable a systematic strategy for design space verification.9 The three-stage approach to develop the mechanistic model involved (a) mechanistic studies toward the description of the elementary steps that define the transformations of interest (Section 5.1), (b) regression of model parameters using an experimental building data set (Section 5.2), and (c) model evaluation using an experimental verification data set (Section 5.3). 5.1. Mechanistic Studies: Description of Elementary Steps. Construction of the mechanistic model began with kinetic studies toward understanding final intermediate formation, consumption of input materials, and the generation of all impurities (in particular, impurities A and B). As described in
Figure 1. Ishikawa diagram for the final intermediate step. C
DOI: 10.1021/acs.oprd.6b00253 Org. Process Res. Dev. XXXX, XXX, XXX−XXX
Organic Process Research & Development
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Scheme 2. Formation of Impurities A−F
5).11 Although impurities C−F were always generated within the purging capability of the process and do not affect the quality of the final intermediate, they are incorporated in the mechanistic model because their formation influences the concentrations of the main reaction components, including impurities A and B.
In addition to reaction parameters, the solubility of compounds 1 and 2 and their corresponding dependencies with temperature were investigated because the coupling reaction mixture forms a slurry comprising the two salts. While the solubility of compound 1 was found to be nearly constant within the studied temperature range, the solubility of compound D
DOI: 10.1021/acs.oprd.6b00253 Org. Process Res. Dev. XXXX, XXX, XXX−XXX
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DynoChem modeling software14 to numerically fit their experimental concentrations until adequate convergence criteria were met. Over the course of the reactions, the concentrations of input 1, input 2, final intermediate, and impurities A−F were measured by HPLC analysis of sample aliquots (10−15 per experiment) and used to fit the model parameters. To ensure that the model building data were spread throughout the parameter space of interest, a design of experiments consisting of a total of 10 reactions were conducted using a central composite design with two center points to assess reproducibility. In these experiments, the four parameters with a high impact on the formation of impurities A and B (i.e., water content, excess molar equivalents of base, deprotonation reaction hold time, and coupling reaction temperature) were varied. The excess equivalents of base were varied from 0.05 to 1.0 equiv, and the temperature was varied from 40 to 65 °C. The amount of input 2, solvent, water from inputs, and reaction time were also varied. Parameter ranges were selected based on existing knowledge to comprise the likely design ranges and to explore potential edges of failure (Table 5). Rate expressions for the elementary reactions used in the model (Table 3) and the final model parameters were regressed in the DynoChem software environment and are shown in Table 6. Characteristic plots of concentration versus time for the formation of final intermediate and growth of impurities A and B are shown in Figure 2. The experimental data corresponds to a reaction performed at 45 °C with 0.80 equiv of excess base (experiment 3, Table 5). Predicted profiles represented by dashed curves result from fitting the mechanistic model to the corresponding reaction conditions. Monitoring the levels of final intermediate reveals peak concentrations that decrease at extended reaction times mainly due to the formation of impurity B. Recall that impurity B is the major impurity generated in the reaction but that it purges to a high degree during crystallization. The growth of impurity B displays two distinct regimes: its initial progress is dictated by the hydrolysis of input 1 promoted by water in the input materials (eq 2, Scheme 2); as the reaction proceeds, impurity B forms at slower rates caused by βelimination of the final intermediate in the presence of excess base (eq 3, Scheme 2). Although impurity A forms to an extent lesser than that of impurity B, the levels of impurity A are of high interest due to its lower purging in the subsequent operations and its capability to lead to an impurity, which may impact drug substance quality. 5.3. Mechanistic Model: Predictive Capability Evaluation. With the mechanistic knowledge described above, the predictive capability of the model was evaluated by comparing its
Table 3. Elementary reactions of the mechanistic model.12 equation
reaction
chemical equation
1 2
reaction of base with water formation of final intermediate final intermediate equilibrium formation of impurity A
t-BuOK + H2O → KOH + t-BuOH compound 1 + input 1 → compound 2 + KCl compound 2 ↔ transient species I + t-BuOK transient species I + compound 1 → impurity A input 1 + KOH → impurity B intermediate + KCl impurity B intermediate + t-BuOK → impurity B + t-BuOH compound 2 + t-BuOK → impurity B + impurity C + t-BuOH compound 1 ↔ transient species II + t-BuOK transient species II + KOH → impurity D + CO2 impurity D + input 1 → impurity E + KCl input 1 + t-BuOK + solvent → impurity F + KCl + t-BuOH
3 4 5 6 7 8 9 10 11
formation of impurity B (hydrolysis) formation of impurity B (hydrolysis) formation of impurities B and C (β-elimination) compound 1 equilibrium formation of impurity D (deprotection) formation of impurity E (impurity D SNAr) formation of impurity F
2 showed a marked dependence on temperature. A correlation of the solubility measured for compound 2 versus temperature using van’t Hoff’s semiempirical equation13 afforded the regressed constants shown in Table 4. The solubility values obtained for compounds 1 and 2 in the actual reaction mixture indicated the need to consider solubility equilibria in the mechanistic model. Table 4. Solubility of the Potassium Salts of Compounds 1 and 2 species
solubility
ln(As)
Bs (kJ/mol)
compound 1 compound 2
0.06 mol/L solubility (mg/mL) = Ase(Bs/RT)
10.53
17.80
5.2. Mechanistic Model: Parameter Regression. A series of model building experiments were carried out to regress the Arrhenius parameters associated with the elementary reactions shown in Table 3. The experimental procedure for the reaction involved (a) adding a heated solution of input 2 to a solution of base to form compound 1, (b) charging input 1 to the mixture and heating at the reaction temperature, (c) aging the reaction mixture, (d) sampling the batch for reaction completion, (e) decreasing the batch temperature and, finally, (f) quenching with an aqueous sodium phosphate solution. Kinetic profiles for each component considered in the model were introduced into the Table 5. Model Building Experiments experiment
base (0.05−1.0 excess equiv)
T (40−65 °C)
input 2 (1.05−1.15 equiv)
solvent (17.0−19.0 L/kg)
water (0.03−0.17 equiv)
time for reaction completion (5−45 h)
1 2 3 4 5 6 7 8 9 10
0.45 0.10 0.80 0.10 0.45 0.80 0.45 0.60 1.00 0.05
52.5 60.0 45.0 45.0 55.0 60.0 40.0 65.0 52.5 52.5
1.05 1.15 1.05 1.05 1.10 1.10 1.10 1.05 1.15 1.10
17.0 19.0 17.0 19.0 17.0 17.0 17.0 17.0 19.0 18.0
0.12 0.14 0.12 0.04 0.03 0.04 0.04 0.04 0.17 0.03
14.2 7.8 22.7 43 10.1 5.2 44.7 5.4 8.9 20.7
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DOI: 10.1021/acs.oprd.6b00253 Org. Process Res. Dev. XXXX, XXX, XXX−XXX
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Table 6. Rate Expressions Used in the Mechanistic Model and Regressed Parameters equation
rate expression
k (L/mol·min)
Ea (kJ/mol)
r = k[base][H2O] r = k[compound 1][input 1] rf = k[compound 2] rr = k[transient species I][base] r = k[transient species I][compound1] r = k[input 1][KOH] r = k[impurity B intermediate][base] r = k[compound 2][ base] rf = k[compound 1] rr = k[transient species II][base] r = k[transient species II][KOH] r = k[impurity D-K3][input 1] r = k[input 1][base]
1 × 10 6.48 × 10−3 8.97 s−1
20 84.6 11.3 53.4 160.6 5.7 16.2 97.0 18.7 34.7 53.5 84.1 148.2
reaction
1 2 3
reaction of t-BuOK with water formation of final intermediate final intermediate equilibrium
4 5 6 7 8
formation of impurity A formation of impurity B (hydrolysis) formation of impurity B (hydrolysis) formation of impurities B and C (β-elimination) compound 1 equilibrium
9 10 11
formation of impurity D (deprotection) formation of impurity E (impurity D SNAr) formation of impurity F
2
4.59 × 10−4 1.53 × 10−2 16.7 6.21 × 10−5 7.16 s−1 6.30 × 10−2 1.92 × 10−3 7.06 × 10−6
Keq (× 103 s−1)
0.54
9.64
Figure 2. (a) Plot of concentration versus time for the consumption of input 2 and formation of the final intermediate. (b) Growth of impurities A and B versus time for the same reaction.
Figure 3. Parity plots of the model verification and building data sets for (a) impurity A and (b) impurity B.
predictions against the experimental values measured in an independent verification data set. The verification data set consisted of six experiments at laboratory scale and eight batches at pilot plant scale that were not used in the regression of the model parameters. Figure 3 shows parity plots of the model building data set and the verification data set for impurities A and B. A qualitative inspection of the plots indicates that the prediction errors in the model building data set and the verification data set are comparable across the range of impurity levels tested. In fact, the root-mean-square error (RMSE) analysis affords no significant variation (
