Development of UV Chemometric Technique for Resolving the Overlapped Spectra of Sacubitril and Valsartan in their Combined Pharmaceutical Dosage Form

 

Sathvika Marwadi¹, Sahil Reddy¹, Vasudha Bakshi¹, Krishnaphanisri Ponnekanti²,

Santhoshi Priya Dandamudi*¹

¹School of Pharmacy, Anurag University, Hyderabad, India.

Department of Pharmaceutical Analysis, Malla Reddy Institute of Pharmaceutical Sciences, Malla Reddy Vishwavidyapeeth (Deemed to be University) Suraram, Hyderabad, India.

 

ABSTRACT:

A UV spectroscopic technique that indicates stability has been developed and evaluated for the assessment of valsartan (VAL) and sacubitril (SAC) in pharmaceutical dosage forms and bulk. The method's robustness, accuracy, precision, and linearity were determined, confirming its appropriateness for routine screening. A linear relationship was established over a concentration range suitable for both compounds, with high correlation coefficients. Recovery studies verified the method’s accuracy, with percentage recoveries falling within the accepted range of 98–102%. This method provided intermediate precision and repeatability with minimal relative standard deviations. Results from the approved UV-Visible spectroscopic method were statistically compared with those from the chemometric analysis of spectroscopic data. Principal Component Regression (PCR) was selected due to its ability to handle complex and correlated data sets efficiently by reducing dimensionality, addressing multicollinearity and improved accuracy. In UV method development, PCR is particularly effective for resolving overlapping spectra and distinguishing between components in multicomponent formulations.

 

 

 

INTRODUCTION:

Sacubitril (SAC) work by inhibiting the enzyme neprilysin, which breaks down peptides that affect blood pressure and inflammation. This increases the levels of peptides in body, which can improve cardiovascular and renal function.¹

 

By inhibiting the body's angiotensin II receptor, the blood pressure-lowering drug valsartan (VAL) lowers blood pressure. This reduces blood pressure and facilitates the heart's ability to pump blood by relaxing blood arteries.²

 

The renin-angiotensin-aldosterone system (RAAS) is blocked by the combination SAC/VAL, which also raises natriuretic peptide levels to treat heart failure. The combined effect relaxes blood arteries, which facilitates the heart's ability to pump blood.³

 

 

Figure 1: Structure of Sacubitril and Valsartan

 

Chemometric techniques are commonly used in spectroscopic examination to ensure the quality of pharmaceuticals, especially when examining combinations including multicomponent dosage forms with overlapping spectrum.⁴

 

Many multi-component techniques, such as inverse least square (ILS), principal component regression (PCR), partial least square (PLS), and classical least square (CLS), are being utilised extensively in quantitative spectrum analysis to extract selected information from the unselective data. These methods are applicable to the research of drug separation, identification, validation, and determination.^5-7

 

A multivariate calibration method called Principal Component Regression (PCR) includes the dimensionality reduction capabilities of Principal Component Analysis (PCA) with the predictive power of linear regression.⁸ The original set of correlated predictor variables, which are frequently spectral data in chemometric applications, are converted into principal components (PCs), a smaller collection of uncorrelated variables, in PCR. In a multivariate linear regression model, these PCs are utilized as predictors since they maintain the majority of the variance found in the original data. Using this approach effectively addresses multicollinearity and reduces noise, making PCR particularly useful in spectrophotometric studies where highly collinear or redundant data is common. By taking the underlying data structure, PCR enhances both the robustness and accuracy of the predictive model.⁹

 

Several spectroscopic studies can be included in multivariate calibration methods, which can significantly improve the accuracy and precision of research. There are numerous chemometric multivariate data methods accessible today, including regression and factor-based approaches like partial least squares regression (PLS) and principal component regression (PCR). They have been extensively documented in the literature for the UV spectroscopic data analysis of multicomponent formulations.^10-13 With their ability to overcome the challenges posed by traditional approaches, out of these two potent chemometric models PCR is applied.

 

For SAC and VAL's independent measurement in pharmaceuticals and biological materials including human plasma and urine, a variety of quantitative analytical techniques have been documented. The stability of SAC and VAL by UV-Spectrophotometric method for determining in synthetic mixtures, as well as its development and validation by RP-HPLC.^14-22 Previously the chemometric approach for simultaneous estimation of SAC and VAL was not reported in the literature.

EXPERIMENTAL WORK:

Materials:

The SAC 24mg and VAL 26mg were film coated tablets produced by Novartis Pharma with the name of Entresto 50mg. All of the raw materials used in producing the tablets met Ph. Eur. quality standards. Both of the purchased standard substances were employed as working standards after being standardised in accordance with the relevant analytical techniques. Analytical-grade chemicals and solvents were utilised (Merck, Germany).

 

Instruments used:

The UV spectra were recorded by Shimadzu 1800 UV-visible spectrophotometer with quartz sample cells measuring 1cm and operating between 200 to 300nm. Data acquisition and spectral processing was performed by using UV Probe. Chemometric Analysis was performed by using the software Unscramble X.

 

Procedures:

Preparation of working standard solution:

Two flasks with a volume of 10mL were taken and filled with 12mg of SAC in one and 13mg of VAL in another, after it had been weighed accurately. Then it was completely dissolved in methanol and the same solvent was added to reach the final volume. A stock solution with concentrations of 120µg/mL SAC and 130µg/mL VAL can be obtained by pipetting 1mL of the aforesaid solution into a 10mL volumetric flask and adding methanol to reach the desired level. Further the ultimate concentration of standard solutions (12µg/mL SAC and 13µg/mL VAL) were prepared by diluting 1mL in 10mL with methanol.

 

Analytical concentration range selection:

The standard stock solution was pipetted into a sequence of 10mL volumetric flasks in the appropriate aliquots. The volume was adjusted with water to create a range of concentration dilutions, from 3-18μg/mL of SAC and 3.25-19.50μg/mL VAL. Measured SAC and VAL at λmax of 224nm and 245nm, the absorbance of the aforementioned solutions was converted to zero order spectra, and a calibration curve of absorbance against concentration was constructed. The correlation coefficient and regression equation were established. In the concentration range of 3-18μg/mL of SAC and 3.25-19.50μg/VAL, Beer Lambert's law was obeyed.

 

Preparation of sample solution:

Take 61mg of SAC and VAL sample transferred into a 10mL volumetric flask and dissolved in methanol and made up to the final volume with the same. Additionally, pipette out one mL of the aforementioned solution into a 10mL volumetric flask and fill it up with methanol. The solution was then filtered using Whatman filter paper no. 40. Distilled water was used to dilute 1 mL to 10mL of this filtrate to prepare the oncentration (SAC 12µg/mL  and VAL 13µg/mL). The assay result was shown as a percentage after these solutions were examined under UV Spectrophotometer.

 

Preparation of degradation sample:

Take 1mL of working standard solution in volumetric flask and add 1mL of degrading agent (Acid, Base, Peroxide, Thermal, Reduction, Hydrolysis and Photolytic). Then refluxed at 60^°C for 1hour and neutralize the solution and make up to 10mL with Methanol. Measure the absorbance of it and calculate the %degradation.

 

Chemometric model application:

Standard solutions of SAC and VAL within their calibration curve range were used for the application of chemometric models built and their spectra were captured between the 200–400nm spectral region. MS-EXCEL was used to insert the spectral data from the standard solutions. The excel spreadsheet created a huge matrix by grouping the drug names and wavelengths into columns and the absorbance and concentrations of the solutions into rows. For the spectral analysis, a wavelength range of 220-300nm with a 5nm data interval was chosen. The Unscramble X software was utilised to import the spectral data of the standard solutions, which were then separated into two groups. The first set, known as the training set, was used to create calibrated models, while the second set, known as the prediction set, was used to forecast the unknown concentrations of solutions made from standards. PCR model was used in the program to make calculations that predicted the amounts in commercial product.

 

RESULTS AND DISCUSSION:

The wavelength range used for the spectral analysis is 220–300nm with a 5nm interval. To prevent correlation between the concentrations of two APIs, the standard solutions were selected at random. After generating training and prediction data sets, the calibration set was optimized with the use of ideal factors. A cross-validation technique using the K-fold approach was used on the calibration data set to determine the ideal number principal components (PCs) for PCR models. The root mean square error (RMSE) values were used to identify errors between the predicted and actual concentrations of each test sample. Since it shows accuracy and precision. The RMSE was calculated following a comparison of the calibration set's actual concentrations with the predicted values.

 

 

 

 

The model with the fewest number of PCs and the lowest RMSE was identified as the best one. Each additional factor was added and the RMSE was recalculated. If the value improved by at least 5%, the variable was kept.

 

The overlay UV spectra of standard solutions of SAC and VAL are shown in Figure 2. Significant overlapping of the spectra of the drug substances can be observed. In addition, the absorbance in the SAC spectrum are significantly lower than that corresponding to VAL.

 

 

Figure 2: Overlay Spectrum of SAC and VAL

 

Method Validation:

Several criteria including linearity, accuracy, precision, and robustness, were examined in accordance with ICH guidelines.²⁴

 

Linearity:

A standard stock solution containing 3-18µg/mL of SAC and 3.25-19.50µg/mL of VAL was used to produce fresh aliquots. The absorbance values of SAC and VAL concentrations were recorded at 224nm and 245nm using Methanol as blank. The correlation coefficient was found to be 0.999 for method. RMSE values shows the average deviation between predicted and actual values is extremely small and indicating the developed method was more accurate.

 

Table 1: Linearity of SAC and VAL

S. No.	Sacubitril		Valsartan
	Conc. (µg/mL)	Absorbance	Conc.(µg/mL)	Absorbance
1	3.00	0.355	3.25	0.424
2	6.00	0.688	6.50	0.751
3	9.00	1.113	9.75	1.046
4	12.00	1.368	13.00	1.435
5	15.00	1.719	16.25	1.782
6	18.00	2.088	19.50	2.180
R2	0.999		0.999
RMSE	0.0040126		0.0044658

 

 

Figure 3: Linear graph of SAC and VAL

 

Accuracy:

By conducting recovery studies in triplicate at three distinct concentration ranges—50%, 100%, and 150%—the established method's accuracy was verified. The accuracy was expressed as a percentage of recovery. With this approach, the accuracy of SAC and VAL %RSD was less than 2. For SAC and VAL, the recovery percentages were between 100.3% and 99.7%. The statistical data fell within the acceptable range as per ICH recommendations.

 

The predicted concentrations of SAC and VAL were compared against their respective reference values and the results are shown in figure 4. For SAC (figure 4A), The PCR model achieved an R² of 0.9936 with a slope of 1.0323 and RMSE of 0.05047, indicating high accuracy and minimal bias. Similarly for VAL (figure 4B), The model exhibited an R² of 0.9999, slope of 0.9997 and RMSE of 0.0045, confirming the excellence accuracy and precision of the model. These results validate the robustness and reliability of the developed PCR-based method for simultaneous quantification. 

 

Accuracy was demonstrated by good correlation between the actual and predicted values at the chosen PCs. PCR model developed was found to be within acceptance criteria for the statistical metrics acquired for the calibration set, including mean, standard deviation (SD), percent RSD and Root mean square errors of calibration (RMSE). This calibration set was therefore refined and applied to the sample solution analysis.

 

 

Table 2: Data representing accuracy of SAC and VAL

Drugs	Conc. of drug (µg/mL)		Added drug %	% Mean recovery	% RSD	RMSE
	Label claim	Pure drug
Sacubitril	24	6.00	50	100.5	0.25	0.0005
		12.00	100	100.0
		18.00	150	100.3
Valsartan	26	6.50	50	99.0	0.61	0.0097
		13.00	100	100.0
		19.50	150	100.1

 

 

Figure 4: Accuracy of SAC and VAL by PCR

 

Precision:

Intra-day and inter-day variance experiments proved the method's precision. Six solutions containing 12μg/mL of SAC and 13μg/mL of VAL were produced for the intra-day variation investigation. These solutions were then tested three times during a single day and the corresponding absorbances were recorded. The percentage RSD was used to illustrate the results. Six solutions containing 12μg/mL of SAC and 13μg/mL of VAL were produced for the inter-day variation investigation. These solutions were then tested three times over three days in a row, and the corresponding absorbance values were collected. The percentage RSD was used to present the results. Using this strategy, the percentage RSD for the intraday and interday precision of SAC and VAL was less than 2 that is within the acceptability criterion, according ICH recommendations.

 

Table 3: System precision of SAC and VAL

	Sacubitril		Valsartan
	%RSD	RMSE	%RSD	RMSE
System Precision	0.28	1.573	0.35	1.585
Method Precision	0.45	1.897	0.36	1.606
Intermediate Precision	0.33	1.721	0.70	1.963

 

Robustness:

Robustness of the method was evaluated by deliberately varying the detection wavelengths by ±5nm from optimized wavelength for both SAC and VAL. The aim was to assess the method’s reliability under slight variations in experimental conditions. The %assay values were calculated at each varied wavelength and presented in Table 4. The results demonstrated minimal variation in assay values across the altered wavelengths. These values indicate that the method remains unaffected by small deliberate changes in wavelength, thus confirming its robustness.

 

Table 4: Robustness results of SAC and VAL

S. No	Sacubitril (λmax=224nm)		Valsartan (λmax =245nm)
	%assay at 229nm	%assay at 219nm	%assay at 250nm	%assay at 240nm
1	99.4	99.7	100.0	99.9

 

Assay:

The amount of API present in the tablet formulation was determined and the results are represented in Table5. The standard and test absorbance values were recorded and the amount found was calculated based on the standard calibration.

 

Table 5: Assay of SAC and VAL (Entresto 50mg) tablets

Drug	Label claim (mg/tab)	Standard absorbance	Sample absorbance	Amount recovered (mg/mL)	% Recovery
Sacubitril	24	1.354	1.346	11.93	99.4
Valsartan	26	1.435	1.412	12.79	98.4

 

The analysis was carried out using a chemometric model to enhance the prediction accuracy and reduce the impact of multicollinearity in absorbance data. The model combines PCR with linear regression, which allows better prediction of concentration even when spectral variables are highly correlated.

 

In this method, multiple absorbance values across a selected wavelength range are considered as predictors (independent variables) and the known concentrations are used as the response variable (dependent variables) during the model training phase. The calibration model was built using standard solutions of known concentrations and principal components were extracted from the absorbance data matrix. These components were then regressed against the concentrations to develop the PCR model. The model was validated with test samples and predicted concentrations were compared against the label claim to determine the accuracy of assay. (Table-5).

 

Forced Degradation:

Forced degradation studies were conducted to assess the stability indicating capability of the developed Using UV spectrophotometry to estimate SAC and VAL simultaneously. Both the drugs were analyzed under different stress conditions as per ICH guidelines and % degradation represented in Table 6. The PCR model efficiently captured the spectral changes due to degradation, enabling precise quantification.

 

Table 6: Forced degradation results of SAC and VAL

Results: % Degradation results	Sacubitril		Valsartan
	Absorbance	Degradation%	Absorbance	Degradation%
Control	1.357	0	1.434	0
Acid	1.216	10.4	1.289	10.1
Alkali	1.195	11.9	1.253	12.6
Peroxide	1.162	14.4	1.227	14.4
Reduction	1.353	0.3	1.276	11.0
Thermal	1.347	0.7	1.389	3.1
Photolytic	1.338	1.4	1.411	1.6
Hydrolysis	1.227	9.6	1.428	0.4

 

CONCLUSION:

SAC and VAL were effectively determined simultaneously using UV spectrophotometric methods based on chemometric approaches for quantitative data processing. To interpret and quantify the spectral data, a chemometric model based on PCR was developed and applied to the UV absorbance data to reduce dimensionality and eliminate multicollinearity, which is common in UV spectra due to overlapping absorbance bands. The application of PCR model to the UV data highlights the robustness and predictive power of the chemometric approach, providing a reliable and stability indicating method for routine quality control and formulation development.

 

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Received on 29.04.2025      Revised on 13.09.2025 Accepted on 01.12.2025      Published on 20.05.2026 Available online from May 25, 2026 Research J. Pharmacy and Technology. 2026;19(5):2153-2159. DOI: 10.52711/0974-360X.2026.00310 © RJPT All right reserved
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