Accepted Manuscript Title: Efficient HPLC Method Development Using Structure-based Database Search, Physico-Chemical Prediction and Chromatographic Simulation Author: Lin Wang Jinjian Zheng Xiaoyi Gong Robert Hartman Vincent Antonucci PII: DOI: Reference:
S0731-7085(14)00524-X http://dx.doi.org/doi:10.1016/j.jpba.2014.10.032 PBA 9784
To appear in:
Journal of Pharmaceutical and Biomedical Analysis
Received date: Revised date: Accepted date:
11-7-2014 17-10-2014 31-10-2014
Please cite this article as: L. Wang, J. Zheng, X. Gong, R. Hartman, V. Antonucci, Efficient HPLC Method Development Using Structure-based Database Search, PhysicoChemical Prediction and Chromatographic Simulation, Journal of Pharmaceutical and Biomedical Analysis (2014), http://dx.doi.org/10.1016/j.jpba.2014.10.032 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Efficient HPLC Method Development Using Structure-based Database Search, Physico-
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Chemical Prediction and Chromatographic Simulation Lin Wanga, Jinjian Zhengb, c, Xiaoyi Gonga, Robert Hartmanb, Vincent Antonuccia
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a.
Merck Research Laboratories, Rahway, New Jersey 07065, USA
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b.
Merck Manufacturing Division, Merck, Rahway, New Jersey 07065,
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Corresponding author. Email: [email protected]. Tel: 732-
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9 ABSTRACT
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Development of a robust HPLC method for pharmaceutical analysis can be very challenging
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and time-consuming. In our laboratory, we have developed a new workflow leveraging
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ACD/Labs software tools to improve the performance of HPLC method development. First, we
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established ACD-based analytical method databases that can be searched by chemical
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structure similarity. By taking advantage of the existing knowledge of HPLC methods archived in
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the databases, one can find a good starting point for HPLC method development, or even reuse
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an existing method as is for a new project. Second, we used the software to predict compound
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physicochemical properties before running actual experiments to help select appropriate
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method conditions for targeted screening experiments. Finally, after selecting stationary and
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mobile phases, we used modeling software to simulate chromatographic separations for
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optimized temperature and gradient program. The optimized new method was then uploaded to
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internal databases as knowledge available to assist future method development efforts. Routine
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implementation of such standardized workflows has the potential to reduce the number of
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experiments required for method development and facilitate systematic and efficient
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development of faster, greener and more robust methods leading to greater productivity. In this
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article, we used Loratadine method development as an example to demonstrate efficient
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method
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28 1. INTRODUCTION
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Analytical testing and control strategy play a critical role during the entire life cycle of the drug
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development process in the pharmaceutical industry. HPLC is the major work horse that has
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been used for all aspects of pharmaceutical analysis including assay, dissolution analysis,
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impurity profile, forced degradation studies, process control, and drug metabolism studies [1-4].
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Given the time constraints and limited resources in an R&D laboratory, it is imperative to
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develop robust HPLC methods quickly to support the drug development process. Many different
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approaches to improve the performance of HPLC method development have been reported [5-
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8]. Often, a column screening system is used to find a promising combination of mobile
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phase/stationary phase which meets desired criteria, and the separation is subsequently
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optimized using a software tool such as DryLab [7-12] or Chromsword [5-6, 13-16]. These
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software tools allow the scientist to model chromatographic separations based upon retention
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data from a limited number of scouting experiments, and optimal separation conditions can be
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predicted by the modeling software. This approach avoids labor intensive trial-and-error
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experiments, potentially resulting in significant improvement in method development efficiency
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and final method quality.
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In spite of the successes with software simulation, there are certain limitations. Typically,
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software simulation is done with one stationary phase and one set of mobile phases. Therefore,
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it is critical to select the appropriate combination of stationary and mobile phases for evaluation
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in scouting experiments. Screening experiments can help the scientist make informed decisions
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on good stationary phase and mobile phase candidates. However, without a good
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understanding of the physicochemical properties of analytes such as pKa, LogP, LogD, and
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solubility, these screening experiments may not yield desired results within a reasonable time
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frame due to excessive trial-and-error experimentation. Ultimately, the chromatographic 3
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experience of the scientist is as important a factor in overall success as the automation and
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simulation tools mentioned above. Therefore, we think that the optimal workflow for efficient
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method development must thoughtfully combine elements of knowledge management, software
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physicochemical property prediction, chromatographic simulation, and focused experimentation.
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Herein, we propose an integrated workflow for HPLC method development shown in Figure 1.
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First, we integrated existing screening results, method information, and vendor applications into
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structure-searchable databases. Knowledge and information is preserved and re-used to
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expedite method development. By searching structure similarity, one can quickly identify a good
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starting point (such as column, pH, and mobile phase) for target analytes. Following that, we
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further leveraged software tools to predict compound physicochemical properties such as
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pKa/logP/logD before running actual experiments. Such information serves as a rule of thumb to
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help select appropriate chromatographic techniques as well as method conditions such as
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stationary phase, buffer pH, and mobile phase additives for targeted and focused screening.
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After the screening experiments to define the stationary and mobile phases, a few scouting
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runs are performed to experimentally verify the in silico selectivity predictions, and separation
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modeling software (e.g. ACD/Labs LC Simulator, DryLab, etc) was used to simulate
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chromatographic separations for rapid development of faster, greener, and more robust
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methods. With the completion of method optimization, the new method is introduced to the
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internal database to assist future method development for a structurally similar compound.
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Herein, we will use an example of Loratadine method development to illustrate how we have
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successfully combined a structure-searchable database tool, physicochemical prediction tool,
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and LC simulator to create a holistic workflow for efficient HPLC method development and
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lifecycle management. Further, the workflow presented also supports a shift from extensive
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high-throughput laboratory screening as the basis of method development, to a model where
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knowledge retrieval, prediction, and simulation suggest optimal analysis conditions which only
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require limited experimental verification in the laboratory before implementation.
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2.1. HPLC Instrumentation
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All experiments were conducted on Agilent HPLC 1100 systems (Agilent, Santa Clara, CA,
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USA) equipped with an autosampler, a quaternary pump and a variable wavelength detector.
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The maximum operating pressure of the system was 400 bar. Empower II software (Waters,
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Milford, MA, USA) was used to control the HPLC system and for data acquisition and analysis.
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XBridge C18 columns (100 mm x 4.6 mm I.D., 3.5 µm) were purchased from Waters, Milford,
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MA, USA.
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Acetonitrile (HPLC grade), water (HPLC grade), Sodium hydroxide solution (10N) and
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triethylamine (HPLC grade) were purchased from Fisher Scientific (Pittsburgh, PA, USA).
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Phosphoric acid (85 wt% in H2O, 99.99% trace metal basis) and Boric acid (99.99%) were
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purchased from Sigma-Aldrich (St. Louis, MO, USA). Loratadine and impurities were
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synthesized by Merck Sharp & Dohme Corp. (Rahway, NJ, USA).
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2.4. Chromatographic Conditions
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Detailed chromatographic conditions are provided in specific figure captions.
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3. RESULTS AND DISCUSSIONS 5
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3.1.
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To improve productivity and reduce R&D costs in an analytical laboratory, significant emphasis
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is placed on high-throughput and multiplexed analyses aimed at generating many experimental
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results in a short period of time. However, one aspect of the method development process that
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is often overlooked is leveraging past experiments to inform current experiments, or simply
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using the power of knowledge management and prediction to assist high throughput
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experimentation.
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analytes (e.g. solvents and reagents) simultaneously within individual groups of a large scientific
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organization, resulting in significant duplication of effort, and the best methods available are not
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always being implemented in each laboratory. Development of optimal analytical methods often
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requires a high level of technical expertise, particularly for challenging low level quantitative
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analyses such as those used for mutagenic impurities [17]. For many organizations, a major
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inefficiency is the one-and-done data life cycle where insights are only captured in the heads of
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individual scientists and not shared, or these insights are stored in an electronic repository with
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limited access across the organization.
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As an example, methods are often developed for commonly observed
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One way to address this problem is to provide a platform for research scientists to capture and
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share their knowledge by combining live analytical data with chemical and structural context. In
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the present research, we have leveraged the commercially available ACD/Web Librarian
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software to establish web-accessible databases that are searchable by structure. Our current
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analytical method databases include achiral and chiral separation methods obtained from
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application notes provided by column manufacturers, literature resources, and analytical
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methods generated within the company. All databases are updated regularly. One major
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advantage of establishing structure searchable databases is that users are able to search by
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chemical structure similarity, which is not feasible by text-based searches. Analytical methods
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for a structurally similar compound can provide a promising starting point for method
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development when a method for the exact compound is not available.
132 As a model system to evaluate the integrated workflow in our laboratory, development of an
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impurity / degradate HPLC profile for Loratadine was selected. Shown in Figure 2 are the
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structures of potential Loratadine related impurities or degradation products. These compounds
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are either listed in compendial monograph (e.g. USP, EP), or identified in the synthetic process.
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Separation of Loratadine and its related compounds has been reported [18]. However, several
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key USP and EP specified impurities were not included in the existing publications. Therefore, a
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new method that is capable of separating all impurities listed in Figure 2 is desired.
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Initiating the proposed workflow, databases were searched for structure similarity (both Markush
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and Simple Structure). There are no records for the separation of exact compounds in the
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database. However, we found multiple records for the separation of compounds with similar
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structures such as protriptyline, nortriptyline, amitriptyline, doxepin, imipramine, desipramine,
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trimipramine, and nordoxepin as shown in Figure 3.. The results from several key applications
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were summarized in Table 1. Most of these methods use either C18 or Cyano stationary phase.
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We chose C18 over Cyano for method development due to its superior column stability, which is
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critical for a commercial supply lab. Buffers with a wide pH range from 2.7 to 12.0 have been
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used as shown in Table 1. Therefore, a column that is stable across a wide pH range is
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preferred. A Waters Xbridge C18 column was selected because it shows excellent peak shape
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and separation for similar compounds, and it is stable across a wide pH range due to its unique
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ethylene bridged hybrid silica.. In several database applications, 0.1% formic acid was used as
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mobile phase modifier. We replaced it with 0.1% phosphoric acid to reduce baseline noise.
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Acetonitrile was selected because it was used in the compendial method and a relevant
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literature [18] for Loratadine and its related compounds.
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156 From preliminary injections using the selected stationary and mobile phases, severe tailing of
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Loratadine peak was observed as shown in Figure 4. The tailing factors range from 1.11 for
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0.005 mg/mL sample solution to 3.54 for 0.5 mg/mL sample solution. One possible root cause is
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the overloading of protonated Loratadine because ionized analyte usually has much less
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loading capacity [19]. As a result, the retention time shifted from 5.56 min for 0.5 mg/mL solution
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to 5.74 min for 0.05 mg/mL solution, and 5.81 min for 0.005 mg/mL solution (The lower the
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concentration, the longer the retention time). Therefore, it is difficult to identify peaks by RRT
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(relative retention time) as the retention time of Active Pharmaceutical Ingredient (API) varies
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with concentration. Clearly, choice of appropriate buffer pH is critical to achieve good peak
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shape, highlighting the value of first understanding basic analyte physicochemical properties
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prior to conducting generalized, broad parameter screens to avoid collection of extensive data
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of limited value.
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For the separation of ionic analytes, controlling mobile phase pH is critical to the performance of
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the method. Typically, the pH should be chosen at least ± 1.5 unit away from pKa’s of all
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components if possible [20]. Although pKa could be estimated by checking the functional groups
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in molecules, this approach is not very accurate due to electronic and/or steric effects. Using
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pKa calculation software, however, pKa values can be calculated with an accuracy of ± 0.3 or
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better in most cases.
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Employing the proposed integrated workflow, the pKa of Loratadine is calculated to be 4.27 by
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ACD software. Based on the rule stated above, a basic buffer (20 mM boric acid with 0.02%
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triethylamine (TEA), pH adjusted to 9.5 with sodium hydroxide) was used to replace the 0.1%
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phosphoric acid as mobile phase A. As shown in Figure 5, excellent peak shape (tailing factor 8
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ranging from 1.03 to 1.05) and constant retention time (retention time ranging from 8.19 min to
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8.21 min) were observed for Loratadine even though its concentration varies from 0.005 mg/mL
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to 0.5 mg/mL, thus significantly improving the consistency of peak identification.
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physicochemical property prediction in hand, we are now better positioned to conduct focused
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experimental verification runs using the selected chromatographic mobile phase and column to
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more efficiently optimize the method.
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After selection of stationary phase, pH, and organic modifier, the next step is to optimize
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gradient and temperature. Traditional trial-and-error experimentation is usually time-consuming,
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and may not be able to yield the optimal method, especially for complex samples as the one
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used in this study. Computer software simulation can help us evaluate separation under new
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conditions within minutes, which improves the efficiency and makes it possible to develop a
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robust method within a reasonable time frame. Computer software assisted HPLC method
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development has been reported since the late 1970s [1, 21]. Currently, a number of software
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programs are commercially available such as DryLab (LC Resources, USA), ChromSword
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(Merck KGaA, Germany), Fusion AE (S-Matrix, USA), and ACD/Labs (Advanced Chemistry
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Development, Canada), and there are many reports on fast method development using such
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software [22-26]. Typically, scouting runs (e.g. 2 runs for Gradient or 4 runs for
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Gradient/Temperature modeling) are first acquired in the laboratory and used to build a
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retention model. Resolution maps show the effect of a given variable on the separation of a set
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of compounds and clearly describe the resolution of the critical pair (least-resolved components)
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as a function of a given variable. The computational results are then utilized as guidance for
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further experimental work in the laboratory. This procedure is repeated until satisfactory
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chromatography is achieved. The chromatogram of the optimized method was shown in Figure
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6. All Loratadine related compounds were baseline separated from each other and from
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Loratadine with a minimum resolution of 1.5. Therefore, this method can be used as an impurity
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profiling method for Loratadine drug substance, or as a stability indicating method for Loratadine
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drug product.
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The method shown in Figure 6 was uploaded to the database, and was later applied to several
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different formulations including tablets, syrup and for different purposes such as stability testing,
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dissolution, and content uniformity with minor modifications. For the content uniformity and
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dissolution testing, the goal is to separate the API from other impurities, but it is not necessary
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to separate impurities from each other. The challenge, however, lies in the large sample size.
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For example, a dissolution test is typically conducted in multiple vessels (e.g. n = 6-24) with
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samples taken at multiple time points for each batch [27]. To determine the content uniformity of
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the dosage form, about 10 to 30 samples are analyzed per batch [28]. Therefore, fast
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separation is desired. Using the retention model built for the impurity profiling method shown in
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Figure 6, we were able to quickly simulate a fast isocratic method to separate Loratadine from
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all other impurities. The experimental chromatogram was shown in Figure 7. Loratadine was
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baseline separated from all other related compounds within 5 minutes with a minimum
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resolution of 2.0, which significantly improved the throughput.
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4. Conclusions
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We have presented an effective workflow for method development employing a combination of
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hardware, software, focused experimentation, and application of personal and organizational
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knowledge. The new workflow consists of three parts. First, we established ACD-based HPLC
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method databases that can be searched by chemical structure similarity to take advantage of
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the existing knowledge of HPLC methods archived in the databases. Second, we used software
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to predict compound physico-chemical properties before running actual experiments to help 10
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select appropriate method conditions for targeted screening experiments. It is worth noting that
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for Loratadine method development, screening of stationary phase and mobile phases were not
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performed as all impurities were well characterized, and a good starting condition was found
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from the database. For complex samples where there are lots of unknown impurities, targeted
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screening may be necessary to find a proming starting condition. Finally, we use the modeling
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software to simulate chromatographic separations, followed by targeted experimental
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verification in the laboratory, to rapidly develop fast and robust HPLC methods. The optimized
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new method was then added to the databases to assist future method development. Such
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workflow brings the benefit of reduction in the number of experiments required for method
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development. Faster, greener and more robust methods are developed in a systematic and
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efficient manner, leading to productivity gains in overall method development.
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245 Acknowledgement
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The authors would like to thank Thom Loughlin, Yong Liu, Xiaodong Bu, and Patrick Chin for
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their support on Merck internal structure-searchable databases; and thank Mary Rogowski and
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Karim Kassam from ACD/Labs for their support.
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Figure Captions
325 Figure 1. A new strategy for efficient HPLC method development.
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Figure 2. Structures of Loratadine (Compound J) and its related compounds. Compound P is
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Pseudoephedrine that is commonly used in combination with Loratadine. Compound F is a
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Pseudoephedrine related compound.
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Figure 3. Structures of compounds with similar structures to Loratadine found in the databases
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including
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trimipramine, and nordoxepin.
nortriptyline,
amitriptyline,
doxepin,
imipramine,
desipramine,
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Figure 4. Overlaid chromatograms of Loratadine API at different concentrations: a = 0.5 mg/mL,
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b = 0.05 mg/mL, c = 0.005 mg/mL. HPLC Column: Waters XBridge C18, 3.5 µm, 100 x 4.6 mm,
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Injection volume: 5 µL. Mobile Phases: A = 0.1% H3PO4, B = Acetonitrile. Flow rate: 1.5 mL/min,
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temperature: 35°C. Detection: UV absorbance at 270 nm. Gradient: 0-10 min, 25-50%B.
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protriptyline,
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Figure 5. Overlaid chromatograms of Loratadine API at different concentrations: a = 0.5 mg/mL,
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b = 0.05 mg/mL, c = 0.005 mg/mL. HPLC Column: Waters XBridge C18, 3.5 µm, 100 x 4.6 mm,
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Injection volume: 5 µL. Mobile Phases: A = 20 mM boric acid + 0.02% triethylamine (TEA), pH
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adjusted to 9.5 with sodium hydroxide, B = Acetonitrile. Flow rate: 1.5 mL/min, temperature:
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35°C. Detection: UV absorbance at 270 nm. Gradient: 0-10 min, 35-65%B.
346 347
Figure 6. Chromatogram of Loratadine and its related compounds. Column: Waters XBridge
348
C18, 3.5 µm, 100 x 4.6 mm, Mobile phases: A = 20 mM boric acid + 0.02% triethylamine (TEA), 15
Page 15 of 27
349
pH adjusted to 9.5 with sodium hydroxide, B = Acetonitrile. Temperature: 35°C. Detection: UV
350
absorbance at 270 nm. Flow rate: 1.5mL/min. Injection volume: 5 µL. Gradient: 0-15 min, 25-
351
35%B; 15-35 min, 35-65%B.
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352 Figure 7. Chromatogram for assay and content uniformity of Loratadine. Column: Waters
354
XBridge C18, 3.5 µm, 100 x 4.6mm, Mobile phases: A = 20 mM boric acid + 0.02% triethylamine
355
(TEA), pH adjusted to 9.5 with sodium hydroxide, B=Acetonitrile. Temperature: 35°C. Detection:
356
UV absorbance at 270 nm. Flow rate: 1.5 mL/min. Injection volume: 5 µL. Isocratic at 59%B.
357
Run time: 5 minutes.
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Table 1. Summary of methods for similar structures from application database Mobile Phase Water/MeOH/ potassium phosphate, pH7.6
NUCLEOSIL® 100-5 C18 HD
Water/Acetonitrile/ TEAphosphate, pH6.9
Ascentis ES Cyano
Acetonitrile, methanol, potassium phosphate, pH7
Good peak shape and separation
Hypersil Gold
Acetonitrile/water /0.1% formic acid, pH2.7
Weak retention. Peak tailing.
ZirChrom-PBD
Acetonitrile/ Water/ potassium phosphate, pH 12.0 Water/Methanol/ Acetonitrile/ Potassium phospate pH7.0
Good peak shape and separation Good peak shape
Zorbax Extend C18
Water/MeOH pyrrolidine buffer, pH11.5 Acetonitrile/ Methanol/ Sodium phosphate, pH7.0
Good separation and peak shape Excellent peak shape and separation
359
XBridge C18
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Pursuit C18
Comments Good resolution. Peak tailing Peak tailing of early eluting peaks
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Column Purospher® STAR RP-18
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Compounds Protriptyline, Nortriptyline, Doxepin, Imipramine, Amitriptyline Protriptyline, Nortriptyline, Doxepine Imipramine, Amitriptyline, Trimipramine Doxepine, Trimipramine, Amitriptyline, Imipramine, Nordoxepin, Nortriptyline, Desipramine, Protriptyline Doxepine, Protriptyline, Imipramine, Nortriptyline, Amitriptyline, Trimipramine, Nordoxepin, Protriptyline, Nortriptyline, Imipramine, Amitriptyline Desmethyldoxepin, Protriptyline, Esipramine, Nortriptyline, Doxepine, Imipramine, Amitriptyline, Timipramine Doxepine, Imipramine, Nortriptyline, Amitriptyline, Trimipramine, Nortriptyline, Imipramine, Amitriptyline, Clomipramine,
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360
HIGHLIGHTS •
Preserved and shared knowledge using structure searchable databases
362
•
Calculated physico-chemical properties for selecting starting initial HPLC parameters
363
•
Optimized complex separations and evaluate method robustness using software
364
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•
365 366
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simulation
Developed an analytical method to separate Loratadine and all known impurities and
us
degradants
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Page 18 of 27
Table 1
Mobile Phase Water/MeOH/ potassium phosphate, pH7.6
NUCLEOSIL® 100-5 C18 HD
Water/Acetonitrile/ TEAphosphate, pH6.9
Ascentis ES Cyano
Acetonitrile, methanol, potassium phosphate, pH7
Good peak shape and separation
Hypersil Gold
Acetonitrile/water /0.1% formic acid, pH2.7
Weak retention. Peak tailing.
ZirChrom-PBD
Acetonitrile/ Water/ potassium phosphate, pH 12.0 Water/Methanol/ Acetonitrile/ Potassium phospate pH7.0
Good peak shape and separation Good peak shape
Zorbax Extend C18
Water/MeOH pyrrolidine buffer, pH11.5 Acetonitrile/ Methanol/ Sodium phosphate, pH7.0
Good separation and peak shape Excellent peak shape and separation
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Pursuit C18
XBridge C18
Comments Good resolution. Peak tailing Peak tailing of early eluting peaks
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Column Purospher® STAR RP-18
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Compounds Protriptyline, Nortriptyline, Doxepin, Imipramine, Amitriptyline Protriptyline, Nortriptyline, Doxepine Imipramine, Amitriptyline, Trimipramine Doxepine, Trimipramine, Amitriptyline, Imipramine, Nordoxepin, Nortriptyline, Desipramine, Protriptyline Doxepine, Protriptyline, Imipramine, Nortriptyline, Amitriptyline, Trimipramine, Nordoxepin, Protriptyline, Nortriptyline, Imipramine, Amitriptyline Desmethyldoxepin, Protriptyline, Esipramine, Nortriptyline, Doxepine, Imipramine, Amitriptyline, Timipramine Doxepine, Imipramine, Nortriptyline, Amitriptyline, Trimipramine, Nortriptyline, Imipramine, Amitriptyline, Clomipramine,
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Table 1. Summary of methods for similar structures from application database
Page 19 of 27
Figure 1
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Database Search
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Physico-chemical Prediction (LogP/LogD/pKa)
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Targeted Screening (stationary phase, solvent, pH) Software-assisted Optimization (gradient, temperature)
Final Method
Page 20 of 27
Figure 2
A
B
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N
N
N
an
H
F
G
OH CH3 HN N CH3
CH3
H Cl Cl
O
N
N
d
CN
CH3
M
Cl
N
N
OH N
O
J
Cl
K Cl N
Cl N
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N
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O
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Cl
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O
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Cl
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HO
Cl
HH N
Cl
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N N O
OH
N
N O
O
H
Page 21 of 27
N O
O
CH3 CH3
O
Nortriptyline
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Protriptyline
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Figure 3
NH
CH3
Amitriptyline
O
NH
CH3 H3C
N
N
CH3
Imipramine
Doxepin
Desipramine
H3C
Trimipramine
CH3
Nordoxepin O
N
N
N H3C
H3C
N CH3
HN CH3
N CH3
NH CH3
CH3
Page 22 of 27
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Figure 4
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Minutes
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Minutes
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Figure 6
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20.00 Minutes
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Figure 7
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*Graphical Abstract
Database Search
Physico-chemical Prediction (LogP/LogD/pKa) Targeted Screening (stationary phase, solvent, pH) Software-assisted Optimization (gradient, temperature) Page 27 of 27
Final Method
S0731-7085(14)00524-X http://dx.doi.org/doi:10.1016/j.jpba.2014.10.032 PBA 9784
To appear in:
Journal of Pharmaceutical and Biomedical Analysis
Received date: Revised date: Accepted date:
11-7-2014 17-10-2014 31-10-2014
Please cite this article as: L. Wang, J. Zheng, X. Gong, R. Hartman, V. Antonucci, Efficient HPLC Method Development Using Structure-based Database Search, PhysicoChemical Prediction and Chromatographic Simulation, Journal of Pharmaceutical and Biomedical Analysis (2014), http://dx.doi.org/10.1016/j.jpba.2014.10.032 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
1
Efficient HPLC Method Development Using Structure-based Database Search, Physico-
2
Chemical Prediction and Chromatographic Simulation Lin Wanga, Jinjian Zhengb, c, Xiaoyi Gonga, Robert Hartmanb, Vincent Antonuccia
3
a.
Merck Research Laboratories, Rahway, New Jersey 07065, USA
5
b.
Merck Manufacturing Division, Merck, Rahway, New Jersey 07065,
6
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c.
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USA
Corresponding author. Email: [email protected]. Tel: 732-
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Page 1 of 27
9 ABSTRACT
11
Development of a robust HPLC method for pharmaceutical analysis can be very challenging
12
and time-consuming. In our laboratory, we have developed a new workflow leveraging
13
ACD/Labs software tools to improve the performance of HPLC method development. First, we
14
established ACD-based analytical method databases that can be searched by chemical
15
structure similarity. By taking advantage of the existing knowledge of HPLC methods archived in
16
the databases, one can find a good starting point for HPLC method development, or even reuse
17
an existing method as is for a new project. Second, we used the software to predict compound
18
physicochemical properties before running actual experiments to help select appropriate
19
method conditions for targeted screening experiments. Finally, after selecting stationary and
20
mobile phases, we used modeling software to simulate chromatographic separations for
21
optimized temperature and gradient program. The optimized new method was then uploaded to
22
internal databases as knowledge available to assist future method development efforts. Routine
23
implementation of such standardized workflows has the potential to reduce the number of
24
experiments required for method development and facilitate systematic and efficient
25
development of faster, greener and more robust methods leading to greater productivity. In this
26
article, we used Loratadine method development as an example to demonstrate efficient
27
method
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development
using
this
new
workflow.
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28 1. INTRODUCTION
30
Analytical testing and control strategy play a critical role during the entire life cycle of the drug
31
development process in the pharmaceutical industry. HPLC is the major work horse that has
32
been used for all aspects of pharmaceutical analysis including assay, dissolution analysis,
33
impurity profile, forced degradation studies, process control, and drug metabolism studies [1-4].
34
Given the time constraints and limited resources in an R&D laboratory, it is imperative to
35
develop robust HPLC methods quickly to support the drug development process. Many different
36
approaches to improve the performance of HPLC method development have been reported [5-
37
8]. Often, a column screening system is used to find a promising combination of mobile
38
phase/stationary phase which meets desired criteria, and the separation is subsequently
39
optimized using a software tool such as DryLab [7-12] or Chromsword [5-6, 13-16]. These
40
software tools allow the scientist to model chromatographic separations based upon retention
41
data from a limited number of scouting experiments, and optimal separation conditions can be
42
predicted by the modeling software. This approach avoids labor intensive trial-and-error
43
experiments, potentially resulting in significant improvement in method development efficiency
44
and final method quality.
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29
45 46
In spite of the successes with software simulation, there are certain limitations. Typically,
47
software simulation is done with one stationary phase and one set of mobile phases. Therefore,
48
it is critical to select the appropriate combination of stationary and mobile phases for evaluation
49
in scouting experiments. Screening experiments can help the scientist make informed decisions
50
on good stationary phase and mobile phase candidates. However, without a good
51
understanding of the physicochemical properties of analytes such as pKa, LogP, LogD, and
52
solubility, these screening experiments may not yield desired results within a reasonable time
53
frame due to excessive trial-and-error experimentation. Ultimately, the chromatographic 3
Page 3 of 27
experience of the scientist is as important a factor in overall success as the automation and
55
simulation tools mentioned above. Therefore, we think that the optimal workflow for efficient
56
method development must thoughtfully combine elements of knowledge management, software
57
physicochemical property prediction, chromatographic simulation, and focused experimentation.
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54
58
Herein, we propose an integrated workflow for HPLC method development shown in Figure 1.
60
First, we integrated existing screening results, method information, and vendor applications into
61
structure-searchable databases. Knowledge and information is preserved and re-used to
62
expedite method development. By searching structure similarity, one can quickly identify a good
63
starting point (such as column, pH, and mobile phase) for target analytes. Following that, we
64
further leveraged software tools to predict compound physicochemical properties such as
65
pKa/logP/logD before running actual experiments. Such information serves as a rule of thumb to
66
help select appropriate chromatographic techniques as well as method conditions such as
67
stationary phase, buffer pH, and mobile phase additives for targeted and focused screening.
68
After the screening experiments to define the stationary and mobile phases, a few scouting
69
runs are performed to experimentally verify the in silico selectivity predictions, and separation
70
modeling software (e.g. ACD/Labs LC Simulator, DryLab, etc) was used to simulate
71
chromatographic separations for rapid development of faster, greener, and more robust
72
methods. With the completion of method optimization, the new method is introduced to the
73
internal database to assist future method development for a structurally similar compound.
74
Herein, we will use an example of Loratadine method development to illustrate how we have
75
successfully combined a structure-searchable database tool, physicochemical prediction tool,
76
and LC simulator to create a holistic workflow for efficient HPLC method development and
77
lifecycle management. Further, the workflow presented also supports a shift from extensive
78
high-throughput laboratory screening as the basis of method development, to a model where
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79
knowledge retrieval, prediction, and simulation suggest optimal analysis conditions which only
80
require limited experimental verification in the laboratory before implementation.
81 2. EXPERIMENTAL
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82 83
2.1. HPLC Instrumentation
85
All experiments were conducted on Agilent HPLC 1100 systems (Agilent, Santa Clara, CA,
86
USA) equipped with an autosampler, a quaternary pump and a variable wavelength detector.
87
The maximum operating pressure of the system was 400 bar. Empower II software (Waters,
88
Milford, MA, USA) was used to control the HPLC system and for data acquisition and analysis.
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XBridge C18 columns (100 mm x 4.6 mm I.D., 3.5 µm) were purchased from Waters, Milford,
92
MA, USA.
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2.3. Materials and reagents
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Acetonitrile (HPLC grade), water (HPLC grade), Sodium hydroxide solution (10N) and
96
triethylamine (HPLC grade) were purchased from Fisher Scientific (Pittsburgh, PA, USA).
97
Phosphoric acid (85 wt% in H2O, 99.99% trace metal basis) and Boric acid (99.99%) were
98
purchased from Sigma-Aldrich (St. Louis, MO, USA). Loratadine and impurities were
99
synthesized by Merck Sharp & Dohme Corp. (Rahway, NJ, USA).
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2.4. Chromatographic Conditions
102
Detailed chromatographic conditions are provided in specific figure captions.
103 104
3. RESULTS AND DISCUSSIONS 5
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105
3.1.
106
To improve productivity and reduce R&D costs in an analytical laboratory, significant emphasis
107
is placed on high-throughput and multiplexed analyses aimed at generating many experimental
108
results in a short period of time. However, one aspect of the method development process that
109
is often overlooked is leveraging past experiments to inform current experiments, or simply
110
using the power of knowledge management and prediction to assist high throughput
111
experimentation.
112
analytes (e.g. solvents and reagents) simultaneously within individual groups of a large scientific
113
organization, resulting in significant duplication of effort, and the best methods available are not
114
always being implemented in each laboratory. Development of optimal analytical methods often
115
requires a high level of technical expertise, particularly for challenging low level quantitative
116
analyses such as those used for mutagenic impurities [17]. For many organizations, a major
117
inefficiency is the one-and-done data life cycle where insights are only captured in the heads of
118
individual scientists and not shared, or these insights are stored in an electronic repository with
119
limited access across the organization.
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Search databases for starting point of method development
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As an example, methods are often developed for commonly observed
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One way to address this problem is to provide a platform for research scientists to capture and
122
share their knowledge by combining live analytical data with chemical and structural context. In
123
the present research, we have leveraged the commercially available ACD/Web Librarian
124
software to establish web-accessible databases that are searchable by structure. Our current
125
analytical method databases include achiral and chiral separation methods obtained from
126
application notes provided by column manufacturers, literature resources, and analytical
127
methods generated within the company. All databases are updated regularly. One major
128
advantage of establishing structure searchable databases is that users are able to search by
129
chemical structure similarity, which is not feasible by text-based searches. Analytical methods
6
Page 6 of 27
130
for a structurally similar compound can provide a promising starting point for method
131
development when a method for the exact compound is not available.
132 As a model system to evaluate the integrated workflow in our laboratory, development of an
134
impurity / degradate HPLC profile for Loratadine was selected. Shown in Figure 2 are the
135
structures of potential Loratadine related impurities or degradation products. These compounds
136
are either listed in compendial monograph (e.g. USP, EP), or identified in the synthetic process.
137
Separation of Loratadine and its related compounds has been reported [18]. However, several
138
key USP and EP specified impurities were not included in the existing publications. Therefore, a
139
new method that is capable of separating all impurities listed in Figure 2 is desired.
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Initiating the proposed workflow, databases were searched for structure similarity (both Markush
142
and Simple Structure). There are no records for the separation of exact compounds in the
143
database. However, we found multiple records for the separation of compounds with similar
144
structures such as protriptyline, nortriptyline, amitriptyline, doxepin, imipramine, desipramine,
145
trimipramine, and nordoxepin as shown in Figure 3.. The results from several key applications
146
were summarized in Table 1. Most of these methods use either C18 or Cyano stationary phase.
147
We chose C18 over Cyano for method development due to its superior column stability, which is
148
critical for a commercial supply lab. Buffers with a wide pH range from 2.7 to 12.0 have been
149
used as shown in Table 1. Therefore, a column that is stable across a wide pH range is
150
preferred. A Waters Xbridge C18 column was selected because it shows excellent peak shape
151
and separation for similar compounds, and it is stable across a wide pH range due to its unique
152
ethylene bridged hybrid silica.. In several database applications, 0.1% formic acid was used as
153
mobile phase modifier. We replaced it with 0.1% phosphoric acid to reduce baseline noise.
154
Acetonitrile was selected because it was used in the compendial method and a relevant
155
literature [18] for Loratadine and its related compounds.
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Page 7 of 27
156 From preliminary injections using the selected stationary and mobile phases, severe tailing of
158
Loratadine peak was observed as shown in Figure 4. The tailing factors range from 1.11 for
159
0.005 mg/mL sample solution to 3.54 for 0.5 mg/mL sample solution. One possible root cause is
160
the overloading of protonated Loratadine because ionized analyte usually has much less
161
loading capacity [19]. As a result, the retention time shifted from 5.56 min for 0.5 mg/mL solution
162
to 5.74 min for 0.05 mg/mL solution, and 5.81 min for 0.005 mg/mL solution (The lower the
163
concentration, the longer the retention time). Therefore, it is difficult to identify peaks by RRT
164
(relative retention time) as the retention time of Active Pharmaceutical Ingredient (API) varies
165
with concentration. Clearly, choice of appropriate buffer pH is critical to achieve good peak
166
shape, highlighting the value of first understanding basic analyte physicochemical properties
167
prior to conducting generalized, broad parameter screens to avoid collection of extensive data
168
of limited value.
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169 3.2 Select Suitable Buffer pH
171
For the separation of ionic analytes, controlling mobile phase pH is critical to the performance of
172
the method. Typically, the pH should be chosen at least ± 1.5 unit away from pKa’s of all
173
components if possible [20]. Although pKa could be estimated by checking the functional groups
174
in molecules, this approach is not very accurate due to electronic and/or steric effects. Using
175
pKa calculation software, however, pKa values can be calculated with an accuracy of ± 0.3 or
176
better in most cases.
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177 178
Employing the proposed integrated workflow, the pKa of Loratadine is calculated to be 4.27 by
179
ACD software. Based on the rule stated above, a basic buffer (20 mM boric acid with 0.02%
180
triethylamine (TEA), pH adjusted to 9.5 with sodium hydroxide) was used to replace the 0.1%
181
phosphoric acid as mobile phase A. As shown in Figure 5, excellent peak shape (tailing factor 8
Page 8 of 27
182
ranging from 1.03 to 1.05) and constant retention time (retention time ranging from 8.19 min to
183
8.21 min) were observed for Loratadine even though its concentration varies from 0.005 mg/mL
184
to 0.5 mg/mL, thus significantly improving the consistency of peak identification.
185
physicochemical property prediction in hand, we are now better positioned to conduct focused
186
experimental verification runs using the selected chromatographic mobile phase and column to
187
more efficiently optimize the method.
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188 3.3 Optimize Separation
190
After selection of stationary phase, pH, and organic modifier, the next step is to optimize
191
gradient and temperature. Traditional trial-and-error experimentation is usually time-consuming,
192
and may not be able to yield the optimal method, especially for complex samples as the one
193
used in this study. Computer software simulation can help us evaluate separation under new
194
conditions within minutes, which improves the efficiency and makes it possible to develop a
195
robust method within a reasonable time frame. Computer software assisted HPLC method
196
development has been reported since the late 1970s [1, 21]. Currently, a number of software
197
programs are commercially available such as DryLab (LC Resources, USA), ChromSword
198
(Merck KGaA, Germany), Fusion AE (S-Matrix, USA), and ACD/Labs (Advanced Chemistry
199
Development, Canada), and there are many reports on fast method development using such
200
software [22-26]. Typically, scouting runs (e.g. 2 runs for Gradient or 4 runs for
201
Gradient/Temperature modeling) are first acquired in the laboratory and used to build a
202
retention model. Resolution maps show the effect of a given variable on the separation of a set
203
of compounds and clearly describe the resolution of the critical pair (least-resolved components)
204
as a function of a given variable. The computational results are then utilized as guidance for
205
further experimental work in the laboratory. This procedure is repeated until satisfactory
206
chromatography is achieved. The chromatogram of the optimized method was shown in Figure
207
6. All Loratadine related compounds were baseline separated from each other and from
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Page 9 of 27
208
Loratadine with a minimum resolution of 1.5. Therefore, this method can be used as an impurity
209
profiling method for Loratadine drug substance, or as a stability indicating method for Loratadine
210
drug product.
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213
The method shown in Figure 6 was uploaded to the database, and was later applied to several
214
different formulations including tablets, syrup and for different purposes such as stability testing,
215
dissolution, and content uniformity with minor modifications. For the content uniformity and
216
dissolution testing, the goal is to separate the API from other impurities, but it is not necessary
217
to separate impurities from each other. The challenge, however, lies in the large sample size.
218
For example, a dissolution test is typically conducted in multiple vessels (e.g. n = 6-24) with
219
samples taken at multiple time points for each batch [27]. To determine the content uniformity of
220
the dosage form, about 10 to 30 samples are analyzed per batch [28]. Therefore, fast
221
separation is desired. Using the retention model built for the impurity profiling method shown in
222
Figure 6, we were able to quickly simulate a fast isocratic method to separate Loratadine from
223
all other impurities. The experimental chromatogram was shown in Figure 7. Loratadine was
224
baseline separated from all other related compounds within 5 minutes with a minimum
225
resolution of 2.0, which significantly improved the throughput.
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226 227
4. Conclusions
228
We have presented an effective workflow for method development employing a combination of
229
hardware, software, focused experimentation, and application of personal and organizational
230
knowledge. The new workflow consists of three parts. First, we established ACD-based HPLC
231
method databases that can be searched by chemical structure similarity to take advantage of
232
the existing knowledge of HPLC methods archived in the databases. Second, we used software
233
to predict compound physico-chemical properties before running actual experiments to help 10
Page 10 of 27
select appropriate method conditions for targeted screening experiments. It is worth noting that
235
for Loratadine method development, screening of stationary phase and mobile phases were not
236
performed as all impurities were well characterized, and a good starting condition was found
237
from the database. For complex samples where there are lots of unknown impurities, targeted
238
screening may be necessary to find a proming starting condition. Finally, we use the modeling
239
software to simulate chromatographic separations, followed by targeted experimental
240
verification in the laboratory, to rapidly develop fast and robust HPLC methods. The optimized
241
new method was then added to the databases to assist future method development. Such
242
workflow brings the benefit of reduction in the number of experiments required for method
243
development. Faster, greener and more robust methods are developed in a systematic and
244
efficient manner, leading to productivity gains in overall method development.
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245 Acknowledgement
247
The authors would like to thank Thom Loughlin, Yong Liu, Xiaodong Bu, and Patrick Chin for
248
their support on Merck internal structure-searchable databases; and thank Mary Rogowski and
249
Karim Kassam from ACD/Labs for their support.
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Its Related Compounds in Ion Pair Reversed Phase HPLC, J. Liq. Chromatogr. Related
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Technol., 33 (2010), 603-614
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[25] T.H. Hoang, D. Cuerrier, S. McClintock, M. Di Maso, Computer-assisted method
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[26] M. Meng, L. Rohde, V. Capka, S. J. Carter, P. K. Bennett, Fast chiral chromatographic
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[27] US Pharmacopeia, Chapter 711, Dissolution.
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[28] US Pharmacopeia, Chapter 905, Uniformity of Dosage Units.
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323 324
Figure Captions
325 Figure 1. A new strategy for efficient HPLC method development.
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326 327
Figure 2. Structures of Loratadine (Compound J) and its related compounds. Compound P is
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Pseudoephedrine that is commonly used in combination with Loratadine. Compound F is a
330
Pseudoephedrine related compound.
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Figure 3. Structures of compounds with similar structures to Loratadine found in the databases
333
including
334
trimipramine, and nordoxepin.
nortriptyline,
amitriptyline,
doxepin,
imipramine,
desipramine,
336
Figure 4. Overlaid chromatograms of Loratadine API at different concentrations: a = 0.5 mg/mL,
337
b = 0.05 mg/mL, c = 0.005 mg/mL. HPLC Column: Waters XBridge C18, 3.5 µm, 100 x 4.6 mm,
338
Injection volume: 5 µL. Mobile Phases: A = 0.1% H3PO4, B = Acetonitrile. Flow rate: 1.5 mL/min,
339
temperature: 35°C. Detection: UV absorbance at 270 nm. Gradient: 0-10 min, 25-50%B.
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M
protriptyline,
340 341
Figure 5. Overlaid chromatograms of Loratadine API at different concentrations: a = 0.5 mg/mL,
342
b = 0.05 mg/mL, c = 0.005 mg/mL. HPLC Column: Waters XBridge C18, 3.5 µm, 100 x 4.6 mm,
343
Injection volume: 5 µL. Mobile Phases: A = 20 mM boric acid + 0.02% triethylamine (TEA), pH
344
adjusted to 9.5 with sodium hydroxide, B = Acetonitrile. Flow rate: 1.5 mL/min, temperature:
345
35°C. Detection: UV absorbance at 270 nm. Gradient: 0-10 min, 35-65%B.
346 347
Figure 6. Chromatogram of Loratadine and its related compounds. Column: Waters XBridge
348
C18, 3.5 µm, 100 x 4.6 mm, Mobile phases: A = 20 mM boric acid + 0.02% triethylamine (TEA), 15
Page 15 of 27
349
pH adjusted to 9.5 with sodium hydroxide, B = Acetonitrile. Temperature: 35°C. Detection: UV
350
absorbance at 270 nm. Flow rate: 1.5mL/min. Injection volume: 5 µL. Gradient: 0-15 min, 25-
351
35%B; 15-35 min, 35-65%B.
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352 Figure 7. Chromatogram for assay and content uniformity of Loratadine. Column: Waters
354
XBridge C18, 3.5 µm, 100 x 4.6mm, Mobile phases: A = 20 mM boric acid + 0.02% triethylamine
355
(TEA), pH adjusted to 9.5 with sodium hydroxide, B=Acetonitrile. Temperature: 35°C. Detection:
356
UV absorbance at 270 nm. Flow rate: 1.5 mL/min. Injection volume: 5 µL. Isocratic at 59%B.
357
Run time: 5 minutes.
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Page 16 of 27
Table 1. Summary of methods for similar structures from application database Mobile Phase Water/MeOH/ potassium phosphate, pH7.6
NUCLEOSIL® 100-5 C18 HD
Water/Acetonitrile/ TEAphosphate, pH6.9
Ascentis ES Cyano
Acetonitrile, methanol, potassium phosphate, pH7
Good peak shape and separation
Hypersil Gold
Acetonitrile/water /0.1% formic acid, pH2.7
Weak retention. Peak tailing.
ZirChrom-PBD
Acetonitrile/ Water/ potassium phosphate, pH 12.0 Water/Methanol/ Acetonitrile/ Potassium phospate pH7.0
Good peak shape and separation Good peak shape
Zorbax Extend C18
Water/MeOH pyrrolidine buffer, pH11.5 Acetonitrile/ Methanol/ Sodium phosphate, pH7.0
Good separation and peak shape Excellent peak shape and separation
359
XBridge C18
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360
an
M
Pursuit C18
Comments Good resolution. Peak tailing Peak tailing of early eluting peaks
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Column Purospher® STAR RP-18
te
Compounds Protriptyline, Nortriptyline, Doxepin, Imipramine, Amitriptyline Protriptyline, Nortriptyline, Doxepine Imipramine, Amitriptyline, Trimipramine Doxepine, Trimipramine, Amitriptyline, Imipramine, Nordoxepin, Nortriptyline, Desipramine, Protriptyline Doxepine, Protriptyline, Imipramine, Nortriptyline, Amitriptyline, Trimipramine, Nordoxepin, Protriptyline, Nortriptyline, Imipramine, Amitriptyline Desmethyldoxepin, Protriptyline, Esipramine, Nortriptyline, Doxepine, Imipramine, Amitriptyline, Timipramine Doxepine, Imipramine, Nortriptyline, Amitriptyline, Trimipramine, Nortriptyline, Imipramine, Amitriptyline, Clomipramine,
d
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17
Page 17 of 27
360
HIGHLIGHTS •
Preserved and shared knowledge using structure searchable databases
362
•
Calculated physico-chemical properties for selecting starting initial HPLC parameters
363
•
Optimized complex separations and evaluate method robustness using software
364
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•
365 366
cr
simulation
Developed an analytical method to separate Loratadine and all known impurities and
us
degradants
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Page 18 of 27
Table 1
Mobile Phase Water/MeOH/ potassium phosphate, pH7.6
NUCLEOSIL® 100-5 C18 HD
Water/Acetonitrile/ TEAphosphate, pH6.9
Ascentis ES Cyano
Acetonitrile, methanol, potassium phosphate, pH7
Good peak shape and separation
Hypersil Gold
Acetonitrile/water /0.1% formic acid, pH2.7
Weak retention. Peak tailing.
ZirChrom-PBD
Acetonitrile/ Water/ potassium phosphate, pH 12.0 Water/Methanol/ Acetonitrile/ Potassium phospate pH7.0
Good peak shape and separation Good peak shape
Zorbax Extend C18
Water/MeOH pyrrolidine buffer, pH11.5 Acetonitrile/ Methanol/ Sodium phosphate, pH7.0
Good separation and peak shape Excellent peak shape and separation
cr
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an
M
Pursuit C18
XBridge C18
Comments Good resolution. Peak tailing Peak tailing of early eluting peaks
ip t
Column Purospher® STAR RP-18
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Compounds Protriptyline, Nortriptyline, Doxepin, Imipramine, Amitriptyline Protriptyline, Nortriptyline, Doxepine Imipramine, Amitriptyline, Trimipramine Doxepine, Trimipramine, Amitriptyline, Imipramine, Nordoxepin, Nortriptyline, Desipramine, Protriptyline Doxepine, Protriptyline, Imipramine, Nortriptyline, Amitriptyline, Trimipramine, Nordoxepin, Protriptyline, Nortriptyline, Imipramine, Amitriptyline Desmethyldoxepin, Protriptyline, Esipramine, Nortriptyline, Doxepine, Imipramine, Amitriptyline, Timipramine Doxepine, Imipramine, Nortriptyline, Amitriptyline, Trimipramine, Nortriptyline, Imipramine, Amitriptyline, Clomipramine,
d
Table 1. Summary of methods for similar structures from application database
Page 19 of 27
Figure 1
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Database Search
M
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Physico-chemical Prediction (LogP/LogD/pKa)
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Targeted Screening (stationary phase, solvent, pH) Software-assisted Optimization (gradient, temperature)
Final Method
Page 20 of 27
Figure 2
A
B
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N
N
N
an
H
F
G
OH CH3 HN N CH3
CH3
H Cl Cl
O
N
N
d
CN
CH3
M
Cl
N
N
OH N
O
J
Cl
K Cl N
Cl N
N
N
M
O
L
Cl
N
O
F
N
te Ac ce p
I
N
us
N
cr
O
E
Cl
Cl
O
N
D
Cl
Cl N
C
Cl
O
O
N O
O
O
N
P
Cl
Cl
O
HO
Cl
HH N
Cl
O
N
N N O
OH
N
N O
O
H
Page 21 of 27
N O
O
CH3 CH3
O
Nortriptyline
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Protriptyline
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Figure 3
NH
CH3
Amitriptyline
O
NH
CH3 H3C
N
N
CH3
Imipramine
Doxepin
Desipramine
H3C
Trimipramine
CH3
Nordoxepin O
N
N
N H3C
H3C
N CH3
HN CH3
N CH3
NH CH3
CH3
Page 22 of 27
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cr
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Figure 4
M
an
0.4
0.1
te
0.2
a
Ac ce p
AU
d
0.3
b c
0.0 4.8
5.1
5.4
5.7
6.0
6.3 Page 23 of 27
Minutes
cr
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Figure 5
an
us
0.6 0.5
M
a
0.2 0.1
te
0.3
Ac ce p
AU
d
0.4
b c
0.0 7.5
7.8
8.1
8.4
8.7
9.0 Page 24 of 27
Minutes
0.008
AU
0.006
Ac ce p
0.010
te
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Figure 6
0.004
P
J
M N
A
B
0.002
C F L I D E G K H
O
0.000 0.00
10.00
20.00 Minutes
30.00
40.00
Page 25 of 27
cr
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Figure 7
an
us
0.0025
M
0.0020
0.0010 0.0005
Ac ce p
AU
te
d
0.0015
J
G
K
0.0000 -0.0005 0.0
1.0
2.0 3.0 Minutes
4.0
5.0
Page 26 of 27
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*Graphical Abstract
Database Search
Physico-chemical Prediction (LogP/LogD/pKa) Targeted Screening (stationary phase, solvent, pH) Software-assisted Optimization (gradient, temperature) Page 27 of 27
Final Method
