*Author:* Sahil Shah.
*Introduction:* Sun Pharmaceutical Industries Limited (Sun Pharma) is an Indian multinational pharmaceutical company headquartered in Mumbai, founded in 1983 by Dilip Shanghvi. It is the largest pharmaceutical company in India and a leading global specialty generic drugmaker with operations in over 100 countries. The company manufactures and markets a wide range of products, including generics, branded generics, specialty medicines, over-the-counter (OTC) products and active pharmaceutical ingredients (APIs). Sun Pharma’s medicines span numerous therapeutic areas, serving both chronic and acute healthcare needs worldwide. It is known for its extensive R&D efforts and broad manufacturing footprint with facilities across multiple continents.
*Objective:* To calculate Beta (β) of Sun Pharmaceuticals Industries Limited and observe its significance.
*Literature Review*
*3D printing of pharmaceutical dosage forms: Recent advances and applications*
Tobias Auel et al (2025) said that, three-dimensional (3D) printing, also referred to as additive manufacturing, is considered to be a game-changing technology in many industries and is also considered to have potential use cases in pharmaceutical manufacturing, especially if individualization is desired. In this review article the authors systematically researched literature published during the last 5 years (2019 – spring 2024) on the topic of 3D printed dosage forms. Besides all kinds of oral dosage forms ranging from tablets and capsules to films, pellets, etc., numerous reports were also identified on parenteral and cutaneous dosage forms and also rectal, vaginal, dental, intravesical, and ophthalmic preparations. In total, more than 500 publications were identified and grouped according to the site of administration, and an overview of the manuscripts is presented here. Furthermore, selected publications are described and discussed in more detail. The review highlights the very different approaches that are currently used in order to develop 3D printed dosage forms but also addresses remaining challenges.
*SolECOs: a data-driven platform for sustainable and comprehensive solvent selection in pharmaceutical manufacturing*
Yiming Ma et al (2025) said that, solvent selection in pharmaceutical crystallization plays a pivotal role in determining overall manufacturing efficiency while also significantly impacting environmental performance and regulatory compliance. A data-driven solution for sustainable solvent selection, applicable to both single and binary solvent systems, was developed and integrated into SolECOs (Solution ECOsystems), a modular and user-friendly platform for Sustainable-by-Design solvent selection in pharmaceutical manufacturing. A comprehensive solubility database containing 1186 active pharmaceutical ingredients (APIs) and 30 solvents was constructed and used in conjunction with thermodynamically informed machine learning models, including the Polynomial Regression Model-based Multi-Task Learning Network (PRMMT), the Point-Adjusted Prediction Network (PAPN), and the Modified Jouyban–Acree-based Neural Network (MJANN), to predict solubility profiles along with associated uncertainties. Sustainability assessment was performed using both midpoint and endpoint life cycle impact indicators (ReCiPe 2016) and industrial benchmarks such as the GSK sustainable solvent framework, enabling a multidimensional ranking of solvent candidates. Experimentally validated case studies involving APIs such as paracetamol, meloxicam, piroxicam, and cytarabine confirmed the approach’s robustness, adaptability to various crystallization conditions, and effectiveness in supporting single and binary solvent screening and design.
*Data Collection:* Historical data of Hindustan Unilever Limited and Nifty50 index data was downloaded from NSE website for the period 1/12/2024 to 30/11/2025. The data was manipulated to get Friday closing prices.
*Data Analysis:* Regression equation with t-statistics for Beta (β)
Weekly return on Equity (Y on X) = -0.111 + 0.402
N = 48 | R square = 0.361 | F = 26.055 | P value = 6.187
The above equation tells us the relationship between weekly return on equity (dependent variable) and weekly return of Nifty50 (independent variable). The positive coefficient of 0.402 indicates a positive relationship between Nifty50 returns and equity returns. This means that if Nifty50 return increases by 1 unit, the equity return increases by approximately 0.40 units and vice versa. The intercept value of -0.111 represents the equity return when the market return is zero. T stat for Beta (β) is 5.104 and the corresponding p-value is 6.18E−06, which is much lower than 0.05 and even 0.01. Hence, the beta coefficient is statistically significant at the 1% level, indicating that market return has a significant impact on equity return. The R² value is 0.361, which means that 36.1% of the variation in equity returns is explained by market (NIFTY) returns, while the remaining 63.9% is due to other factors. The F-statistic is 26.055 with a p-value of 6.18E−06, indicating that the overall regression model is statistically significant.
*Conclusion:*
Since the Beta value (0.402) is less than 1, the stock is less volatile than the market and moves in the same direction as the market. Hence, it is relatively safer compared to the market but may generate lower returns during strong market upswings.
*Reference:*
Tobias Auel et al (2025). Yiming Ma et al (2025).