Stability Indicating High Performance Liquid Chromatography Method Development Validation of Alectinib in Bulk Drug and Pharmaceutical Dosages Form

Need of Development of Analytical Methods:

Newer analytical methods are developed for the drugs or the drug combination because of the following reasons:

• The drug or the drug combination may not be official in any pharmacopoeia.

• A literature search may not reveal an analytical procedure for the drug or its combination.
• Analytical methods may not be available for the drug combination form of the formulation due to interference caused by the excipients.
• Analytical methods for the quantification of the drug or the drug combination from biological fluids may not be available.
• Method may be available for simultaneous estimation but it may be tedious, time consuming & expensive.
• There may be a need for an alternative method to confirm for legal or scientific reasons, analytical data originally obtained by existing methods.

The newly developed methods find their importance in various fields   such as:

• • • Research institutions
    • Quality control departments
    • Approved testing laboratories
    • Biopharmaceutics & bioequivalence studies

The combination dosage forms are complex in nature because of different additives and individual drug components present in it, in the process of estimation it is important to confirm that one component does not interfere with the estimation of the other. Hence the analytical approach should be different.

1.2 Techniques used in Instrumental Methods:

There are many techniques available for the analysis of materials; however, they all are based on the materials interaction with energy. This interaction permits the creation of a signal that is subsequently detected and processed for its information content. Chemical instrumentation includes the following principle types:

1.2.1 Spectroscopic techniques:

Spectroscopy measures the interaction of the material with electromagnetic radiation. Different types are ultraviolet and visible spectrophotometry, fluorescence and phosphorescence spectrophotometry, atomic spectrometry (emission and absorption), infrared spectrophotometry, raman spectroscopy, X-ray spectroscopy, nuclear magnetic resonance spectroscopy and electron spin resonance spectroscopy.

1.2.2 Electrochemical techniques:

Electrochemistry measures the interaction of the material with an electric field. Different types are potentiometry, voltametry, amperometric technique, coulometry electrogravimetry and conductometric techniques.

1.2.3 Chromatographic techniques:

Separation processes are used to decrease the complexity of material mixtures. The most utilized separation method is chromatography. Mainly following type of techniques are used High –performance liquid chromatographic techniques (HPLC), High –performance thin layer chromatography (HPTLC) and Gas chromatography (GC).

1.2.4 Mass Analysis:

Mass spectrometry measures the interaction of charged materials and electric and magnetic fields.

1.2.5 Thermal Analysis:

Calorimetric and thermo gravimetric analysis measure the interaction of material and heat. In order to utilize the techniques available currently, complex material mixtures must be separated into simpler samples for individual analysis.                                       

1.2.6 Hyphenated Techniques:

Combinations of the above techniques are called as "hyphenated" techniques. Several examples are in popular use today and new hybrid techniques are under development. Few examples of these techniques are: GC-MS (Gas chromatography – mass spectrometry), GC-IR (Gas chromatography-infrared spectroscopy), MS-MS (Mass spectrometry-mass spectrometry) and LC – MS (Liquid chromatography-mass spectroscopy). When a sample is available for analysis, then attention must be given on most suitable and effective technique to be employed from many of the available methods for the required determination and this main decision is taken by the analyst.

1.3 Introduction Of Chromatography: ^12-14             

Chromatography is the technique by which solutes of two or more component are separated by dynamic differential migration process, in a system consist of two phases, one of which moves continuously in a given direction and in which the individual component exhibits different mobilites due to difference in adsorption or partition, molecular size etc. The word chromatography is derived from Greek letters chromos meaning color and graphy means color writing. The purpose of preparative chromatography is to separate the components of a mixture. Analytical chromatography is done normally with smaller amounts of material and is for measuring the relative proportions of analytes in a mixture.

1.3.1 Introduction of High-Performance Liquid Chromatography (HPLC):

HPLC is a popular method of analysis because it is easy to learn and use and is not limited by the volatility or stability of the sample compound. It is used for speedy resolution of complex mixtures, separation and determination of species in a variety of organic, inorganic and biological materials. The time of retention is important and is depend upon the nature of the components. The interaction of the solute with mobile and stationary phase can be manipulated through different choices of both solvents and stationary phases.             

Instrumentation of HPLC:

Figure 1.1: Basic Instrumentation of HPLC

The basic parts of HPLC: -

1. Mobile Phase Supply System:

The solvent reservoirs can be filled with a range of solvents of different polarities, provided they are miscible or they can be filled with solutions of different pH and are mixed in buffer volume. The solvents must be pure and be degassed to avoid formation of gas bubbles. Typical flow rates are 1 to 2 ml/min for conventional 4.6mm i.d. columns. Solvents used for HPLC must be of “HPLC” grade, that is, the solvents that have been filtered through 0.2 µm filters. This extends the pump life by preventing scoring and reduces contamination and plugging of the column. Because the particles that are used to pack HPLC columns are small enough (<50>12: Mechanical pumps and Pneumatic pumps of the mechanical pumps, the most frequently used is the reciprocating pump. Pneumatic pumps are either gas displacement type, which uses direct pressure from highly compressed gas to force the solvent out of the tube, or the pneumatic amplifier type in which the compressed gas at a lower pressure impinges on the large end of the piston to force the smaller end to deliver the liquid. The pneumatic pumps have the advantage of pulse less operation. The most commonly used pump for the HPLC system reciprocating pump (Fig No. 6)

Figure 1.2: Reciprocating Pump

2) Sample Injection System:

Samples can be introduced manually in to the valve with the syringe to fill the sample loop.

Figure 1.3: Manual Sample Injection System

Table 1.2: Characteristics to be validated in HPLC.

Quality by Design (QbD) is increasingly becoming an important and widely used term in the pharmaceutical industry quality system. QbD can be considered to be a holistic, system-based approach to the designing and developing formulation and manufacturing processes which ensures predefined product specifications.

In 2002, in order to establish a more systematic and risk based approach to the development of pharmaceutical products, using the progresses in science and technology, Food and Drug Administration (FDA) announced the “cGMP for the 21st Century: A Risk based Approach” Initiative. This initiative, focused on QbD, and the publication of the Process Analytical Technology (PAT) Guidance in 2004 by the FDA contributed decisively for the modernization of the pharmaceutical industry and challenged them to look beyond the traditional approach of Quality by Testing (QbT). In addition to these new ideas, three important guidance documents were published  as part of International Conference on Harmonization (ICH) guidelines: Q8 Pharmaceutical Development and Q9  Quality Risk Management, in 2005, and ICH Q10 Pharmaceutical Quality System, in 2008. These guidance documents implemented together, in a holistic manner, provides an effective system that emphasizes a harmonized science and risk-based approach to product development, assuring an improving in Quality in pharmaceutical industry.

In ICH Q8 guidance, the concept of QbD was mentioned, stating that “quality cannot be tested into products, i.e., quality should be built in by design”. In 2009, the ICH Q8 guidance was reviewed, clarifying key concepts of the original guidance. Additionally, the principles of QbD were describes and QbD defining as “a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management”.

This framework represents a move away from the traditional approach in the industry of QbD and was relatively new to the pharmaceutical industry at the beginning of the twenty- first century. However, it can be found the application of some principles of QbD across the industry long before then, but in an isolated way. Table 6 compares the current state to the desired QbD state. In fact, QbD is a comprehensive approach targeting all phases of drug discovery, manufacture, and delivery. The aim is to improve the quality  and reduce  the costs of medicines for the consumer. This may be an interactive systematic approach and thus the circular design as shown in Figure 9.This circle of QbD can be divided into two general areas, product knowledge and process understanding. These two areas meet in the design space and the interaction of product knowledge and process understanding allows for continuous improvement.

Figure 1.5: Quality by Design concept

QbD begins by defining the desired product performance and also by defining a product that meets those performance requirements. The characteristics of the desired product are the basis for designing the manufacturing process, which needs to be monitored in terms of performance. Each of these steps influences each other, continuing the cycle. The inner circle interacts with many other specific measures of pharmaceutical manufacturing, such as specifications, critical process parameters, ensuring the product knowledge and process understanding. The underlying principles of QbD are explained in the quality guidelines of international conference on harmonization i.e. ICH Q8 Pharmaceutical Development, ICHQ9 Quality Risk Management, and ICH Q10 Pharmaceutical Quality System. Fig. No. 10 presents the guidelines that explain QbD.

Figure 1.6: ICH Q8/Q9/Q10 triangle in QbD paradigm

The application of QbD presents several advantages and can be summarized as:

• Patient safety and product efficacy are focused
• Scientific understanding of pharmaceutical process and methods is done
• It involves product design and process development
• Science based risk assessment is carried
• Critical quality attributes are identified and their effect on final quality of product is analyzed
• It offers robust method or process
• Business benefits are also driving force to adopt QbD.

  1.6.2 Quality by Design

 Means that product and process performance characteristics are scientifically designed to meet specific objectives, not merely empirically derived from performance of test batches.

Figure 1.7: Flow diagram of QbD approach to method development

The approach provides an in-depth knowledge and enables the creation of a chromatographic database than can be utilized to provide alternative method if required, in future

• Development of a robust method.
• Understand, reduce and control sources of variability.
• Applicable throughout the life cycle of the method.
• Regulatory flexibility Movements within “Analytical Design Space”.

1.6.3 There are two types of QbD?

 1) Manually

2) Software

1.6.4 QbD to HPLC Method development

a. Dependent factors

i) Instrument operating parameters

• Column
• Flow rate

  ii) Method development Parameters

• Mobile Phase  
•  Mobile phase Composition
•  pH

b. Independent factors

i) Sample preparation variations

• Solvents of stock solution
• Solvents of sub-dilutions

1.6.5 Elements of Quality Design

ICH guideline Q8 refers all elements of pharmaceutical development included in QbD. In a marketing authorization application, the Pharmaceutical Development section is projected to provide a complete understanding of the product and manufacturing process. The aim of this section is to design a quality product and its manufacturing process to consistently deliver the intended performance of product. The information and knowledge gained from pharmaceutical development studies and manufacturing experience provide scientific understanding to support the establishment of the specifications, and manufacturing controls. During pharmaceutical development, QbD  suggests that it  should include the following elements:

• • Defining the quality target product profile (QTPP)
  • Identifying potential critical quality attributes (CQAs)
  • Link raw material attributes and process parameters to CQAs and perform risk assessment
  • Developing a design space
  • Designing and implementing control strategy
  • Continuous improvement

1) Defining Product Design Requirements and Critical Quality Attributes

The product design requirements must be well understood in the early design phase, and they can be found in a Quality Target Product Profile (QTPP). The QTPP is derived from the desired product information and it has been defined as “a prospective summary of the quality characteristics of a drug product that ideally will be achieved to ensure the desired quality, taking into account safety and efficacy of the drug product”. Therefore, pharmaceutical companies construct a target product profile that describes: Intended use in clinical setting, route of administration, dosage form, delivery Systems Dosage strength(s), Container closure system

Therapeutic moiety release or delivery and attributes affecting, Pharmacokinetic characteristics (e.g., dissolution, aerodynamic performance) Drug  product  quality  criteria  like  sterility, purity, stability  and  drug  release  as appropriate for dosage form the intended for marketing. The QTPP guides scientists to establish strategies and keep the product developing effort focused and efficient. In addition to defining the requirements to design the product, the QTPP will help identify critical quality attributes (CQAs). ICH Q8 defines CQA as “a physical, chemical, biological, or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality”.CQAs are generally linked with the drug substance, excipients, intermediates (in-process materials) and drug product. Quality risk management tools, found in the ICH Q9 guideline, are often used to identify and prioritize the potential CQAs. Relevant CQAs can be identified by a dynamic process quality risk management and experimentation that evaluates the extent to which their variation can have an impact on the ultimate quality product. The accumulated experience, the knowledge obtained from similar products and from literature references are essential to make these risk assessments. Taken together, this data provides a rationale that links the CQA with the safety and efficacy of the product. The outcome of the risk assessment would be a list of CQAs ranked in order of importance. The potential CQAs can be modified when the formulation and manufacturing processes are selected and as product knowledge and process understanding increase.

2) Quality Risk Management in QbD

Risk management has become a priority process in the pharmaceutical industry with the advances in the QbD. As seen before, QbD is based on sound science and quality risk management. It is a systematic approach to development that begins with predefined objectives and an emphasis on product process understanding and process control. In order to achieve this, a risk management process has to be a priority. Quality risk management is a systematic process for the assessment, control, communication and review of risks to the quality of the drug product across the product lifecycle. ICH Q9 discusses the role of risk management in pharmaceutical industry. For pharmaceutical development, ICH Q9 suggests the application of the principles and tools of quality risk management to: Select the optimal product design and process design. Enhance knowledge of product performance over a wide range of material attributes, processing options, and process parameters Assess  the  critical  attributes  of  raw  materials,  solvents, Active  Pharmaceutical Ingredient (API), starting materials,APIs, excipients, or packaging materials to  establish  appropriate  specifications,  identify  critical  process  parameters  and establish manufacturing controls decrease variability of quality attributes Assess the need for additional studies relating to scale up and technology transfer make use of the “design space” concept (see ICH Q8).

Quality risk management supports a scientific and practical approach to decision- making, assessing the probability, severity and sometimes detectability of the risk. In pharmaceutical development, risk assessment is important in identifying which material attributes and process parameters potentially have an effect on product CQAs – Critical Material Attributes (CMAs) and Critical Process Parameters (CPPs). Risk assessment is typically performed early in the pharmaceutical development process and is repeated as more information becomes available and greater knowledge is obtained.

Risks to quality can be assessed in a variety of informal ways (empirical and / or internal procedures) based on, for example, compilation of observations, trends and other information. Such approaches continue to provide useful information that might support topics such as handling of complaints, quality defects, deviations and allocation of resources. Additionally, the pharmaceutical industry can evaluate the risk using recognized risk management tools. Some of these tools are:

Basic risk management facilitation methods (flowcharts, check sheets, cause and effect diagram, etc.)

• Failure Mode Effects Analysis (FMEA)
• Failure Mode, Effects and Criticality Analysis (FMECA)
• Fault Tree Analysis (FTA)
• Hazard Analysis and Critical Control Points (HACCP)
• Hazard Operability Analysis (HAZOP)
• Preliminary Hazard Analysis (PHA)
• Risk ranking and filtering.

These tools might be adapted for use in specific areas to drug substance and drug product quality. Also, quality risk management methods and some supporting statistical tools can be used in combination. Combined use provides flexibility that can facilitate the application of quality risk management principles.

The statistical tools can support and facilitate quality risk management. They can enable effective data assessment, aid in determining the significance of the data set(s), and facilitate more reliable decision making. Example of statistical tool are Design of Experiments, Control Charts, Histograms, etc.

1.6.6 Design of Experiments (DoE)

Traditional pharmaceutical development approaches are often limited by experiments that test one-at-a-time variability. Comprehensive Design of Experiments uses multidisciplinary teams to design and execute soundly based statistical designs to gain a full understanding of the product and its manufacturing process. The output of DoE confirms CQAs and CPPs that need to be controlled in the manufacturing process.

In an experiment, one or more factors are deliberately changed in order to observe the effect on one or more response variables. This may lead to an extend number of experiments. In DoE, it is ensured that the selected experiments produce the maximum amount of relevant information, keeping costs low by conducting few experiments.

Created by Sir Ronal A. Fisher in the 1920s and 1930s, DoE is defined as a structured and efficient statistical method for planning experiments, so that the data obtained can be analyzed to yield valid and objective conclusions and for determining the relationships among the factors affecting a process and its output. DoE initiates with defining the objectives of an experiment and selecting the process factors for the study. An experimental design is the laying out of a detailed experimental plan in advance of doing the experiment. The statistical theory underlying DoE generally begins with the concept of process models, and the most common it is the process model of the “black box” type, with several discrete or continuous input factors that can be controlled and one or more measured output responses, as shown in Figure 12. The measured responses describe the properties of the investigated system. By changing the most influential factors (e.g. amount of disintegrant, time of mixture, force of compression) the features of the system might be altered according to a response (e.g. disintegration time, content uniformity, hardness). Frequently, the experiments are affected by a number of uncontrolled factors that may be discrete, such as different machines or operators, and/or continuous such as ambient temperature or humidity.

Once factors have been chosen and responses measured, it is desirable to get an understanding of the relationship between them, that is, linking the changes in the factors to the changes in the responses with a mathematical model. In fact, the base for DoE is an approximation of reality with the help of a mathematical model. This model is never 100% right, but simply helps to transport the complexity of the reality into an equation which is easy to handle. The most common empirical mathematical models fit to the experimental data take are polynomial functions, usually in a linear form or quadratic form. The choice of an experimental design is an important part of a DoE process, being critical for the success of the study. This choice depends on a number of aspects, including the nature of the problem and study (e.g., a screening, optimization, or robustness study), the factors and interactions to be studied (e.g., four, six, or nine factors, and main effects or two- way interactions), and available resources (e.g., time, labour, cost, and materials). Numerous statistical experimental designs are known.The following list gives the commonly used design types:

1. Full factorial design
2. Box-Behnken design
3. Central composite design
4. Doehlert design

1. Full two-level factorial designs

Full two-level factorial design is an experimental matrix that has limited application in RSM when the factor number is higher than 1 because the number of experiments required for this design (calculated by expression N = 2k, where N is experiment number and k is factor number) is very large, thereby losing its efficiency in the modeling of quadratic functions. Because a complete three-level factorial design for more than two variables requires more experimental runs than can usually be accommodated in practice, designs that present a smaller number of experimental points, such as the Box–Behnken, central composite, and Doehlert designs, are more often used. However, for two variables, the efficiency is comparable with designs such as central composite. The majority of applications of three-level factorial designs are in the area of chromatography^.

2. Box–Behnken designs

Box and Behnken suggested how to select points from the three-level factorial arrangement, which allows the efficient estimation of the first and second-order coefficients of the mathematical model. These designs are, in this way, more efficient and economical then their corresponding 3k designs, mainly for a large number of variables. Its principal characteristics are: (1) Requires an experiment number according to N = 2k (k ? 1) + cp, where k is the number of factors and (cp) is the number of the central points; (2) All factor levels have to be adjusted only at three levels (?1, 0, +1) with equally spaced intervals between these levels. This experimental design has been applied for the optimization of several chemical and physical processes; however, its application in analytical chemistry is still much smaller in comparison with central composite design.

3. Central composite design

The central composite design was presented by Box and Wilson.  This design consists of the following parts:

• A full factorial or fractional factorial design;
• An additional design, often a star design in which experimental points are at a distance ? from its center;
• A central point.

 Fig. 13 (a) and (b) illustrates the full central composite design for optimization of two and three variables. Full uniformly routable central composite designs present the following characteristics:

 1. Require an experiment number according to N = k 2 + 2k + cp, where k is the factor number and (cp) is the replicate number of the central point;

 2. ?- values depend on the number of variables and can be calculated by ? = 2(k ? p)/4. For two, three, and four variables, they are, respectively, 1.41, 1.68, and 2.00;

3.  All factors are studied in five levels (??, ?1, 0, +1, + ?).

 4) Doehlert design

Developed by Doehlert, the design is a practical and economical alternative in relation to other second-order experimental matrices. This design describes a circular domain for two variables, spherical for three variables, and hyper spherical for more than three variables, which accents the uniformity of the studied variables in the experimental domain. Although its matrices are not routable as previous designs, it presents some advantages, such as requiring few experimental points for its application and high efficiency. Other characteristics are presented below:

1. Requires an experiment number according to N = k2 + k + cp, where k is the factor number and (cp) is the replicate number of the central point;

2. Each variable is studied at a different number of levels, a particularly important characteristic when some variables are subject to restrictions such as cost and/or instrumental constraints or when it is interesting to study a variable at a major or minor number of levels;

3.   The intervals between its levels present a uniform distribution;

For two variables, the Doehlert design is represented by a central point surrounded by six points from a regular hexagon as shown in Fig.14.

Applications of the Doehlert design in analytical chemistry are increasing in recent years, mainly because of its advantageous characteristics in relation to other designs.  Application of response surface methodology in the optimization of analytical procedures is today largely diffused and consolidated principally because of its advantages to classical one-variable-a-time optimization, such as the generation of large amounts of information from a small number of experiments and the possibility of evaluating the interaction effect between the variables on the response. The central composite design is still the symmetrical second- order experimental design most utilized for the development of analytical procedures. The application of three-level factorial designs is not frequent, and the use of this design has been limited to the optimization of two variables because its efficiency is very low for higher numbers of variables. However, the Box–Behnken and Doehlert designs present more efficient matrices and have increased the number of published works in recent years. Multiple response optimization using desirability functions have until now had its utilization limited to the chromatographic field, its related techniques, and to electrochemical methods. However, its principles can be applied to the development of procedures using various analytical techniques, which demand a search for optimal conditions for a set of responses simultaneously.

1.6.7 Design Space and Control Strategy

A key concept in the QbD paradigm is Design Space – a multidimensional space that encompasses combinations of process inputs (material attributes and process parameters) and the CQAs that provide assurance of suitable product performance. ICH Q8 (R2) guideline introduces the concept of Design Space to the pharmaceutical industry and defines it as “the multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality.” A Design Space is a way to represent the product and process understanding which will be establish.The product and process understanding and Design Space helps to identify and explain the all sources of variability and thus way out from this variability by measuring and controlling the CPPs and CMAs responsible for variability. Finally, this assignment predicts the accurate and reliable product quality attributes within specifications in terms of quality.

Once a sufficient level of product and process understanding is achieved, through Design Space, a Control Strategy should be developed that assures that the process will remain in control within the normal variation in material attributes and process operating ranges. Figure 8 shows how Control Strategy are connected and interact with Design Space and Knowledge Space. Control Strategy is defined as “a planned set of controls, derived from current product and process understanding that ensures process performance and product quality. The controls can include parameters and attributes related to drug substance and drug product materials and components, facility and equipment operating conditions, in-process controls, finished product specifications, and the associated methods and frequency of monitoring and control.” A Control Strategy is designed to ensure that a product of required quality will be produced consistently. The elements of the control strategy should describe and justify how in-process controls and the controls of input materials (drug substance and excipients), intermediates (in-process materials), container closure system, and drug products contribute to the final product quality. These controls should be based on product, formulation and process understanding and should include, at a minimum, control of the CPPs and CMAs. In a QbD approach, pharmaceutical development will generate process and product understanding and identify sources of variability. This sources of variability may impact on product quality and therefore should be identified, understood, and subsequently controlled. Product and process understanding, in combination with quality risk management, will support the control of the process such that the variability can be compensated for in an adaptable manner to deliver consistent product quality Scale-up, technology transfer and manufacturing experience can lead to refinements of the control strategy.

1.6.8 Continuous improvement throughout product life cycle

QbD focuses on building quality into the product and manufacturing processes, as well as continuous process improvement. Continuous improvement of a product and process should be employed throughout the lifecycle of a product.

ICH Q10 describes a model for the establishment of an effective Pharmaceutical Quality System (PQS) that can be used by manufacturers implementing QbD systems and can evaluate and improve product quality throughout the product lifecycle. In fact, PQS facilitate continual improvement, helping the identification and implementation of appropriate product and process quality improvements, reducing the variability, and identifying and prioritizing areas for continual improvement. It is important to share the knowledge gained during development and implementation that is relevant for utilization of that Design Space on the manufacturing floor and under the PQS. This knowledge can include results of risk assessments, assumptions based on prior knowledge, and statistical design considerations. Linkages among the Design Space, Control Strategy, CQA and QTPP are an important part of this shared knowledge. In the case of changes to an approved design space, appropriate filings should be made to meet regulatory requirements. Movement within the approved design space, as defined in the ICH Q8 (R2) glossary, does not call for a regulatory filing. For movement outside the design space, the use of risk assessment could be helpful in determining the impact of the change on quality, safety and efficacy and the appropriate regulatory filing strategy.

• MATERIAL & METHODS:

Table 1.2: Active Pharmaceutical Ingredient

Table 1.3: List of Chemicals

Table 1.4: List of Instruments

4 Preparation of Ammonium format Buffer Solution

6.8 gm of Potassium dihydrogen orthophosphate was dissolved in sufficient water (HPLC grade) with aid of sonicator. Then add triethylamine or orthophosphoric acid was used to adjust the pH to 5.

1.4.5 Preparation of stock solutions

10 mg of Alectinib diluted with 10mL Acetonitrile in volumetric flask to get concentration of 1000 µg/ml. From the resulting solution 0.1 ml was diluted to 10 ml with Acetonitrile to obtain concentration of 10 µg/ml of Alectinib.

1.4.6 Chromatographic procedure

Chromatographic separations were carried out on Analytical column: C18 column Waters X Bridge (4.6× 250mm id. particle size 5µm), UV detection: 265 nm, Injection volume: 20 µL, Flow rate: 1.00 mL min -1, Temperature: Ambient, Run time: 10 min

A mixture of acetonitrile, Ammonium Format Buffer (pH-5) and methanol (90:10) was used as the mobile phase. The pH of the buffer solution was adjusted by phosphoric acid (H3PO4) and triethylamine (TEA). The wavelength of 265 nm was used as detection at which both drugs gave good response.

1.4.7 Experimental Design

TRIAL 1:

Figure No. 6.5: Chromatogram of standard solution of Alectinib Trial 1

Discussion: Poor peak shape, Broadening and tailing of peak. More retention time as expected. Baseline is not proper as per consideration. Peak properties not match with standards hence peak is rejected.

TRIAL 2:

Discussion: Poor peak shape, Broadening and tailing of peak observed. More retention time. Baseline is not proper. Peak properties not match with standards hence peak is rejected.

TRIAL 3:

Figure No. 6.5: Chromatogram of standard solution of Alectinib Trial 3

Discussion: Good peak shape, Broadening or tailing not observed. Baseline is good. Peak properties extremely match with standards hence peak is accepted, Optimized Chromatographic condition.

Chromatographic conditions

Mobile phase: Ammonium format buffer: Acetonitrile (30.00:70.00v/v), pH of buffer: 5, Analytical column: C₁₈ column Waters XBridge (4.6× 250mm id. particle size 5µm), UV detection: 265 nm, Injection volume: 10 µL, Flow rate: 1.00 mL min ^-1, Temperature: Ambient, Run time: 10 min.

1.5.6 Analytical Validation

2 Level Factorial Design was employed to develop the RP-HPLC method for estimation of Alectinib in bulk and formulation. We optimized chromatographic conditions by design expert 8 software and the optimized conditions were as follows.
Mobile phase: Ammonium format buffer: Acetonitrile (30.00:70.00v/v), pH of buffer: 5, Analytical column: C₁₈ column Waters XBridge (4.6× 250mm id. particle size 5µm), UV detection: 265 nm, Injection volume: 10 µL, Flow rate: 1.00 mL min ^-1, Temperature: Ambient, Run time: 10 min.  

5. Analytical Method Development

Different mobile phases were investigated to develop the suitable HPLC method for the analysis of Alectinib in formulations. For the selection of media the criteria employed was sensitivity of the method, ease of sample preparation, miscibility of the drug, cost of solvents and applicability of method to various purposes. Retention time and peak area of Alectinib in the selected medium at respective wavelengths were determined and compared with the reference standards and formulation also.

Figure 1.4: A typical chromatogram of Alectinib

The applied chromatographic conditions permitted a good separation of Alectinib in a short retention time of 4.217 min. (Fig. 1.4), no drug decomposition was observed during the analysis. The LC method was validated for the parameters reported below.

5. System suitability study

According to USP, system suitability parameters are an essential part of analytical methods. Therefore, Retention time, peak area, and theoretical plates were stated for standard solutions. A sample of 25µg/ml was run at 265 nm and the achieved data was compared with the prescribed limit. Results were shown in the table. 5.27.

Table.1.13: System suitability parameters

Preparation of Calibration Curve

The stock solution was prepared using 10mg of Alectinib in 10ml of Acetonitrile and further solutions were diluted with the mobile phase. Furthermore, the Final solutions were sonicated about 5 min. The linearity range was taken from 5 to 25µg/ml and plotted calibration curve was found to be linear with 0.9978. A calibration curve is shown in fig. 5.5 and details are in table 5.28. 

Figure 5.5: Linearity graph for Alectinib

Figure 1.6: Chromatogram of Alectinib (5 µg/ml)

Figure 1.7: Chromatogram of Alectinib (10 µg/ml)

Figure 1.8: Chromatogram of Alectinib (15 µg/ml)

Figure 1.9: Chromatogram of Alectinib (20 µg/ml)

Figure 1.10: Chromatogram of Alectinib (25µg/ml)

Figure 1.11: Chromatogram of Alectinib (30µg/ml)

5. Selectivity

Brand name of Alectinib Formulation is Alecensa 150 mg. 10 capsules of Alectinib formulation without shells were triturated in a mortar pestle and 14.23mg (10 mg of pure drug) including other excipients was shifted in 10 ml volumetric flask and volume were adjusted with Acetonitrile. Retention time and another peak parameter of the formulation were compared with the API of the drug. A chromatogram is shown in figure 5.11. Retention time, Area, Asymmetric factor, and Theoretical Plates were found to be 4.217 min, 349455, 1.131 and 10031 respectively. These results concluded that the excipient of the capsule does not interfere with the separation of the drug in the selected mobile phase.

Figure 1.11: A typical chromatogram of Alectinib [Concentration 5 ug/ml]

5. Sensitivity

The sensitivity of the method can be determined by the limit of detection and the limit of quantitation. Signal to noise ratio is of paramount importance in the calculation. Six replicates of the blank sample were run and calculate noise level which is three times of LOD and ten times of LOQ respectively.

Table 1.16: Result of Sensitivity

5. ---

   Accuracy

To develop a routine analytical method, accuracy has great importance invalidation. It is nothing but a comparative study of the reference value and found value. Accuracy existed between specified ranges of 99.92 to 100.36%. It was conducted by preparing the five replicates of three different concentrations and estimated the found concentration and % Recovery. The results obtained are shown in table 4.

Table 1.17: Accuracy results of Alectinib by RP-HPLC

Precision is the series of measurements in different environments of all day. We performed intraday precision and Interday precision. Six replicates of 20 µg/ml solution were prepared and took 6 readings in a day right from morning to evening. Intraday precision proved that the method remains stable in different climatic conditions of the day. Relative standard deviation % was found to be 1.5652 likewise Interday precision is the sample analysis for three days and relative standard deviation % was found to be 1.730.  Analytical precision yields reliable results and complies with the ICH guideline. Repeatability was performed by preparing the six replicates and RSD% was 0.6940. The results obtained are shown in table 5.30.

Table 1.18: System Precision results for Alectinib by RP-HPLC.

Chromatogram of Alectinib Esilate in capsule formulation showed a peak at a retention time of 4.217 min. The mobile phase designed for the method resolved the drug very efficiently and The Retention time of Alectinib was 4.217± 0.0018 min. The detection of wavelength was 265 nm. The peak properties of a formulation containing the drug were compared with standard and it was observed that the peak was adequately resolved without the interference of the excipient. Recovery was achieved at 99.94%. The result was shown in table 5.31.

Table 1.

Demonstration of precision was done under two categories. The injection repeatability (System Precision) was assessed by using six injections of the standard solution of Alectinib and the % RSD of the replicate injections was calculated. Results obtained are shown in table 100.

Robustness is a measure of capacity of a method to remain unaffected by small, but deliberate variations in the method conditions, and is indications of the reliability of the method. A method is robust, if it is unaffected by small changes in operating conditions. To determine the robustness of this method, the experimental conditions were deliberately altered at three different levels and retention time and chromatographic response were evaluated. One factor at a time was changed to study the effect. Variation of mobile phase composition (70:30 v/v), flow rate by 1 ml/min (0.9 and 1.1 ml/min), Variation of wavelength by 265 nm (263 nm and 267 nm), Variations in pH by 5 (4.8 and 5.2) had no significant effect on the retention time and chromatographic response of 10 ?g/ml solution, indicating that the method was robust, The result was shown in table

Table 1.17: Result of Robustness

Recovery

The The recovery of the method was evaluated by the addition of standard at three levels 80, 100, and 120 % to the mixture. Six replicates were run and calculated the mean amount of drug recovered and percentage recovery. It found that results were observed within the prescribed limit. Results were shown in table 8.

Part-II

Development and Validation of Stability Indicating Liquid Chromatographic Methods for Alectinib and Its Formulation

According to the above prescribed, the RP-HPLC method was developed and validated as per ICH guideline. Optimized mobile phase by design expert software was used to estimate drug in human plasma. Details are as follows.

1.6 Optimized chromatographic conditions

Mobile phase: Ammonium format buffer: Acetonitrile (30.00:70.00v/v), pH of buffer: 5, Analytical column: C₁₈ column Waters XBridge (4.6× 250mm id. particle size 5µm), UV detection: 265 nm, Injection volume: 10 µL, Flow rate: 1.00 mL min ^-1, Temperature: Ambient, Run time: 10 min.

• RESULT AND DISCUSSION:

5. Hydrolysis

• Acid Degradation Studies

Approximately 1 mL of solution of stock Alectinib was added, followed by 1 mL of 1 N hydrochloric acid, which was then refluxed for 30 minutes at 60⁰C/40⁰C for 1 to 5 sampling Days. A 400 ppm and a 50 ppm solution were prepared from the resulting solution, and 0.30 ?L solutions were injected into the system, and the chromatograms were recorded in order to determine the stability of the sample.

Table 1. Software has given Conditions for forced degradation studies.

Results of Hydrolysis by 1 N HCl at 40⁰C

The percentage degradation of Alectinib was found to be 8.33 % in acidic condition. The percentage degradation was within acceptable criteria (NMT 10%). The extra peaks were eluted at retention times of 1.994 min and the chromatogram was shown in Figure

Day 05

Results of Hydrolysis by 1 N HCl at 40⁰C

The percentage degradation of Alectinib was found to be 12.33% in acidic condition. The percentage degradation was within acceptable criteria (NMT 10%). The extra peaks were eluted at retention times of 2.012min and the chromatogram was shown in Figure

Day 01

Results of Hydrolysis by 1 N HCl at 60⁰C

The percentage degradation of Alectinib was found to be 9.98% in acidic condition. The percentage degradation was within acceptable criteria (NMT 10%). The extra peaks were eluted at retention times of 1.996 min and the chromatogram was shown in Figure

Day 05

Results of Hydrolysis by 1 N HCl at 60⁰C

The percentage degradation of Alectinib was found to be 14.33% in acidic condition. The percentage degradation was within acceptable criteria (NMT 10%). The extra peaks were eluted at retention times of 2.017 min and the chromatogram was shown in Figure

Conclusion: Less degradation will exist 40⁰C and Day 1^st also all runs shown percentage degradation is less than 10%. The percentage degradation was within acceptable criteria (NMT 10%). 2FI Model shown that ANOVA values are significant.

• Alkali Degradation Studies

Approximately 1 mL of solution of stock Alectinib was added, along with 1 mL of 1 N sodium hydroxide, and the mixture was refluxed for 30 minutes at 60⁰C. It was necessary to dilute the resulting solution to create 400 parts per million and 50 parts per million solutions, which were then injected into the system and the chromatograms were recorded to determine the stability of the sample.

Results of Hydrolysis by 1 N NaOH at 40⁰C

The percentage degradation of Alectinib was found to be 0.00% in basic condition. The percentage degradation was within acceptable criteria (NMT 10%). No extra peaks were eluted in given run time and the chromatogram was shown in Figure

Day 05

Results of Hydrolysis by 1 N NaOH at 40⁰C

The percentage degradation of Alectinib was found to be 0.00% in basic condition. The percentage degradation was within acceptable criteria (NMT 10%). No extra peaks were eluted in given run time and the chromatogram was shown in Figure

Day 01

Results of Hydrolysis by 1 N NaOH at 60⁰C

The percentage degradation of Alectinib was found to be 0.00% in basic condition. The percentage degradation was within acceptable criteria (NMT 10%). No extra peaks were eluted in given run time and the chromatogram was shown in Figure

Day 05

Results of Hydrolysis by 1 N NaOH at 60⁰C

The percentage degradation of Alectinib was found to be 0.00% in basic condition. The percentage degradation was within acceptable criteria (NMT 10%). No extra peaks were eluted in given run time and the chromatogram was shown in Figure

Conclusion: Alectinib contain the no of Nitrogen so that basicity of drug is more so that if Alectinib is being exposed to alkali 1 N NaOH, no major degradation has been seen accordingly because nature both natures are same, degradation never happened if pH of the drug and solvent are the same.

5. Peroxide Degradation Studies

1 mL of Alectinib solution of stocks, as well as 1 mL of hydrogen peroxide (H₂O₂) at a concentration of 20 Percentage, was added individually. The solutions were maintained at 25 to 60 degree Celsius for 30 minutes.

As part of the HPLC investigation, the resulting solution was diluted to create 400 ppm and 50 ppm solutions, and 0.30 ?L was put into the system, with chromatograms recorded to determine sample stability. One prominent degradent peak was observed.

Day 01

Results of Oxidation by 20 Percent H₂O₂ at 60⁰C

The percentage degradation of Alectinib was found to be 57.718% in Oxidative condition. The percentage degradation was not within acceptable criteria (NMT 10%). One extra peak were eluted at 8.210 min in given run time and the chromatogram was shown in Figure

Day 05

Results of Oxidation by 20 Percent H₂O₂ at 60⁰C

The percentage degradation of Alectinib was found to be 65.298 % in Oxidative condition. The percentage degradation was not within acceptable criteria (NMT 10%). One extra peak were eluted at 8.210 min in given run time and the chromatogram was shown in Figure

Results of Oxidation by 20 Percent H₂O₂ at 25⁰C

The percentage degradation of Alectinib was found to be 44.936 % in Oxidative condition. The percentage degradation was not within acceptable criteria (NMT 10%). One extra peak were eluted at 8.210 min in given run time and the chromatogram was shown in Figure

Day 05

Results of Oxidation by 20 Percent H₂O₂ at 25⁰C

The percentage degradation of Alectinib was found to be 52.029 % in Oxidative condition. The percentage degradation was not within acceptable criteria (NMT 10%). One extra peak were eluted at 8.210 min in given run time and the chromatogram was shown in Figure

Conclusion: Less degradation will exist on 25⁰C and for one sampling day is 44.936 also all runs shown percentage degradation is more. 2FI Model shown that ANOVA values are significant because P Values was found to be 0.005.

Thermal degradation studies

The standard medication solution was baked in an oven at 30°C to 105°C for 6 hours to investigate dry heat degradation from 1^st day to 5^th Day. HPLC analysis was carried out by diluting the resulting solution to 400 parts per million (ppm) and 50 parts per million (ppm) solutions, injecting 0.30 ?L of the solution into the system and recording the chromatograms to determine the stability of the sample. No major degradation was observed.

Results of Thermolysis  at 30⁰C

The percentage degradation of Alectinib was found to be 2.13% in thermolytic condition. The percentage degradation was within acceptable criteria (NMT 10%). One extra peaks were eluted on 7.893 min in given run time and the chromatogram was shown in Figure

Day 05

Results of Thermolysis at 30⁰C

The percentage degradation of Alectinib was found to be 42.00% in thermolytic condition. No extra peaks were eluted on 7.893 min in given run time and the chromatogram was shown in Figure

Day 01

Results of Thermolysis  at 105⁰C

The percentage degradation of Alectinib was found to be 22.33% in thermolytic condition. No extra peaks were eluted on 7.893 min in given run time and the chromatogram was shown in Figure

Day 05

Results of Thermolysis at 105⁰C

The percentage degradation of Alectinib was found to be 31.94% in thermolytic condition. No extra peaks were eluted on 7.891 min in given run time and the chromatogram was shown in Figure

Conclusion: If Alectinib is being exposed to different temperature , less degradation exist at 30 C temperature and degradation was found to be 2.13%.

Photolytic degradation studies

Additionally, the photochemical stability of the medication was investigated by exposing the solution (4000 ppm & 500 ppm) to UV Light for three days in a UV chamber or by using 200 Watt hours/m2 in a photochemical stability chamber. HPLC was used to test for sample stability. The resulting solution was diluted to generate 400 ppm and 50 ppm solutions. Then 0.30 ?L of each solution was injected into the system and the chromatograms were recorded to determine if the sample had retained its stability. No major degradation observed.

Results of Photolysis by Light 1x ICH

The percentage degradation of Alectinib was found to be 23.11% in Photolytic condition. The extra peaks were eluted at retention times of 5.262 min and the chromatogram was shown in Figure

Day 05

Results of Photolysis by Light 1x ICH

The percentage degradation of Alectinib was found to be 61.4% in Photolytic condition. The extra peaks were eluted at retention times of 5.262 min and the chromatogram was shown in Figure

Day 01

Results of Photolysis by Light 3x ICH

The percentage degradation of Alectinib was found to be 44.89 % in Photolytic condition. The extra peaks were eluted at retention times of 5.262 min and the chromatogram was shown in Figure

Day 05

Results of Photolysis by Light 3x ICH

The percentage degradation of Alectinib was found to be 79.07% in photolytic condition. The extra peaks were eluted at retention times of 5.262 min and the chromatogram was shown in Figure