Drug Development Starts with Measurements

Analytical Method Strategies in Drug Development

Welcome to My Blog series in drug development

In this blog, I will talk about analytical strategies in drug development, method development, validation, two-dimensional liquid chromatography (2DLC), troubleshooting, and how the information acquired during this process can help the formulation, manufacturing, and overall, the drug development process. To develop a drug product (DP) one needs to understand the physicochemical properties of the active pharmaceutical ingredient (API) before developing any analytical methods. All drug development activities start with measurements and your decisions are based on data you get from sample analysis. Therefore, it is imperative that your analytical methods are robust and free of biases. The phase appropriate analytical development and validation does not mean “quick and dirty”, rather than less validation attributes and wider acceptance criteria at that specific phase of development. It is costly to redevelop a method at later phases, and the cross-over to the new method could be problematic. Stay tuned for more blogs and follow

Steps for method development and validation

In drug development, the analytical group needs to support all cross-functional areas of CMC from early development to late development and regulatory filings. There are certain data from drug discovery that is useful to start with, e.g., physicochemical properties of the API, storage requirements, and shelf life. For biologics, a key information is the isoelectric point (pI) and the amino acid (AA) sequence of the molecule. This will help in the design of the experiments for the analytical methods and help in identifying the functional groups’ liabilities of the molecule. Most analytical methods are chromatographic based methods. Therefore, this blog will address this area. The approach, however, can be adapted to nonchromatographic methods by dropping and adding specific validation attributes to your design. The steps in analytical development that need to be followed are:

· Sample preparation

· Column selection

· Temperature-gradient optimization

· Prevalidation

· Validation

· Method transfer

· Troubleshooting

Forced degradation sample preparation

The importance of sample preparation resides in the fact that you need stable and reproducible samples during the time of analytical development and validation. To develop a stability indicating method, you need to generate forced degradation samples that have the same profile repeatedly and not changing in time. To achieve this requirement is a challenge and you need to have some basic knowledge of name reactions and mechanisms of degradation to stop the reaction at the primary step.

The focus is only to have stable forced degradation samples during method development and validation. You can achieve this by refrigeration or freezing. The ID of the peaks is not important at this stage, just the chromatographic separation.

For small molecule, the forced degradation conditions are much stronger. ICH Q1A(R2) recommends that stress testing be done on a single batch of the drug substance and includes the effect of temperatures (e.g., 50 C, 60 C, etc.), humidity (e.g., 75% RH or greater), oxidation, hydrolysis across a wide range of pH, and photolysis. The standard conditions for photostability testing are described in ICH Q1B. For regulatory submission, a summary from forced degradation studies is required by ICH M4Q(R1).

Column Selection

The heart of the chromatographic separation is the analytical column. A wrong choice in the column selection will lead to a poor method and no subsequent gradient – temperature optimization can fix this. Let’s consider the reversed-phase (RP) HPLC, or RPLC columns. The basic equations behind column selection are the resolution and the hydrophobic subtraction model.

Resolution (R) is a function of efficiency (N), retention factor of the second peak (k) and selectivity (𝛼).


The hydrophobic subtraction model of column selectivity (L. R. Snyder, J. W. Dolan, P. W. Carr, J. Chromatogr. A, 2004, 1060, 1–2, 77-116) has been developed to characterize a given RP stationary phase, based on five parameters (H, S*, A, B, and C) that describe the physicochemical nature of that phase.

H - phase hydrophobicity

S* - resistance of the stationary phase to penetration by a solute molecule (steric effect)

A - hydrogen-bond acidity of the phase

B - hydrogen-bond basicity of the phase

C - interaction of the phase with ionized solute molecules (coulombic)

The column selection should be based on the total number of resolved peaks, resolutions, peak widths, and peak shapes. Always start with the number of resolved peaks to rank the columns. You may need to do some compromise between critical pair resolution, peak widths, and peak shapes. If the separation is not adequate, chose an orthogonal column with opposite descriptors (H, S*, A, B, and C) but focus only on one at a time. Here comes your knowledge of physicochemical properties of the solute and what is the predominant mechanism of interaction with the stationary phase. For this decision, look at their structure and predict the most likely separation mechanism and the associated column descriptor.

To determine if a column is orthogonal, calculate the Fs factor (Eq. 1). Columns of FS factor higher than 50 are considered orthogonal (I.A. Haidar Ahmad, Chromatographia, 80 (2017) 705-730). Column databases such as provide tools to compare chromatography columns based on their selectivity. This database contains around 700 stationary phases and provide a method to compare the selectivity of the stationary phases based on the FS factor which is the distance between columns (column1 and column 2) in five-dimensional space.

(Eq. 1)

The advantage of the spider plot (Fig. 1) is that it provides information on which selectivity parameters are different. This is helpful when searching for the right column when the analyst knows the physicochemical properties of the critical-pair separation.

Fig.1 Spider plots comparing selectivity of different HPLC columns

(Tam, J., Ahmad, I.A.H., Blasko, A., J. Pharm. Biomed. Anal. (2018), 155, 288–297, DOI: 10.1016/j.jpba.2018.03.067)

Temperature-Gradient Optimization

Once the column is selected, the temperature - gradient optimization may begin. For a complex peaks system (e.g., triple combination products) the optimization can benefit from a software assisted analytical method development, like AutoChrom MS (ACD/Labs, Advanced Chemistry Development, Inc.). The flow chart and the predicted/experimental chromatograms are illustrated in Fig. 2 and Fig. 3, respectively. However, an experienced analyst may achieve satisfactory separations without it for limited number of peaks.

Fig. 2 Method development strategy (left) and Process in AutoChrom MS to extract elution information from LC/MS and LC/UV data files (right). At various steps in the process, users have the option to review data and make appropriate corrections.

Fig. 3 Predicted (top) and experimental (bottom) chromatograms (LC-UV) of the optimized gradient. The peaks are labeled with their m/z values.

(Blasko, A., Tam, J., Ahmad, I.A.H., Gunasekera, S., Oshchepkova, I., Galin, A., Vazhentsev, A, Tashlitsky, V., Adams, D., J. Appl. Pharm. (2018), 10:260, DOI: 10.4172/1920-4159.1000260)

Method validation and transfer

Once the method is optimized, the validation follows. During development you have a pretty good idea how your method performs and always have in mind that after development, the validation will occur. There are validation characteristics described in the ICH Q2(R1) guideline that need to be followed. Revision work for ICH Q2(R1), Q2(R2)/Q14 EWG is in progress. The new Analytical Procedure Development guideline (Q14) will be for S4, P4 and P5 of CTD and will complement with Q8(R2) and Q11.

The acceptance criteria for validation will differ at early and late phases of drug development and for small molecules vs. biologics. The method transfer is a validation with somewhat wider acceptance criteria and different labs.

Two-Dimensional Liquid Chromatography (2DLC) in pharmaceutical analysis

In research, two-dimensional liquid chromatography (2DLC) is being used for a while. Various phases have been coupled in 2DLC. Some examples include size-exclusion chromatography (SEC) coupled to reversed-phase liquid chromatography (RPLC) for the identification of phenolic compounds (C.T. Scoparo, et all, J. Chromatogr. A, 1222 (2012) 29-37), SEC:RPLC for unconjugated small molecule drugs and related small molecule impurities in antibody drug conjugates (Y. Li, C. Gu, et all, J. Chromatogr. A, 1393 (2015) 81-88), hydrophilic interaction liquid chromatography (HILIC) coupled with RPLC-MS for lipid analysis (P. Dugo, et all, J. Chromatogr. A, 1278 (2013) 46-53; S. Wang, et all, J. Chromatogr. A, 1321 (2013) 65-72), as well as cation exchange chromatography (CEX) coupled with RPLC-MS for the characterization and identification of partially digested and reduced monoclonal antibodies (G. Vanhoenacker, et all, Anal. Bioanal. Chem., 407 (2015) 355-366).

Recently the 2DLC started to be used in GMP settings and routinely used for QC analysis, especially for biologics.

Fig. 4 Schematic of two-dimensional liquid chromatography

Dissolution can fail due to multiple factors

Dissolution is an important part of drug performance and is also a stability indicating method. Often, the dissolution sample analysis is performed by HPLC. I would like to address factors that can derail your method development and validation.

• Factors related to physicochemical properties of the drug

• Solubility

• Particle size

• Solid phase characteristics (amorphic state, crystallinity, polymorphism)

• Factors related to drug product formulation

• Granulating agents and binders

• Disintegrants and diluents

• Lubricants

• Method of granulation (wet, dry)

• Compression force

• Factors related to the dosage form

• Drug excipient (physical) interaction

• Disaggregation

• Effect of test parameters, analytical methods, apparatus choice

• Guiding the shaft, vibration, alignment, temperature

• Dissolution medium:

• pH, surface tension, viscosity

• Miscellaneous factors

• Adsorption, sorption, humidity, detection error

(Remington: The Science and Practice of Pharmacy 22nd Ed, 2006, pg. 436)

Analytical support and troubleshooting

The customers of the analytical development work are the QC, formulation development and manufacturing groups. There is a tendency to blame the analytical group for any out of specification/out of trend data as well as for formulation performance. Therefore, solid analytical method validation with good intermediate precision is required.

Even after a successful validation, one may encounter unexpected results. Ghost peaks can appear in stability sample analyses and the origin of these peaks must be addressed in a timely manner. We encountered ghost peaks from nitrile glove contamination when analyzing low dose formulations (Tam, J.; Blasko, A., LCGC North America (2016), 34(1), 50-55).

A common task for the analytical team and required by the manufacturing site is the cleaning verification of the manufacturing equipment. Non-dedicated equipment should be cleaned at product change over to prevent cross-contamination. For this task, appropriate analytical methods need to be developed. Cleaning verification methods in pharmaceutical industry must be validated to demonstrate the ability to recover contaminants from the surface of equipment. Due to hard-to-reach areas of some equipment, stainless steel coupons (e.g., 50 cm^2) are used to represent equipment surfaces for cleaning verification experiments in the laboratory. The method variability and the recovery from stainless steel coupons made the analysts anxious during method development and validation. The major contributor to low and variable recoveries of API residues from stainless steel coupons was traced to the lack of a well-defined procedure for cleaning of the coupon surfaces (Ahmad, I.A.H., Tam, J., Li, X., Duffield, W., Tarara, T., Blasko, A., J. Pharm. Biomed. Anal. (2017), 134, 108-115.; Ahmad, I.A.H., Blasko, A., J. Vis. Exp. (126), e56175, DOI: 10.3791/56175 (2017)).

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