The pharmaceutical industry faces significant challenges when promising drug candidates fail during development due to suboptimal ADME (absorption, distribution, metabolism, excretion) properties or toxicity concerns. Natural compounds are subject to the same pharmacokinetic considerations. In silico approaches offer a compelling advantage—they eliminate the need for physical samples and laboratory facilities, while providing rapid and cost-effective alternatives to expensive and time-consuming experimental testing. Computational methods can often effectively address common challenges associated with natural compounds, such as chemical instability and poor solubility. Through a review of the relevant scientific literature, we present a comprehensive analysis of in silico methods and tools used for ADME prediction, specifically examining their application to natural compounds. Whereas we focus on identifying the predominant computational approaches applicable to natural compounds, these tools were developed for conventional drug discovery and are of general use. We examine an array of computational approaches for evaluating natural compounds, including fundamental methods like quantum mechanics calculations, molecular docking, and pharmacophore modeling, as well as more complex techniques such as QSAR analysis, molecular dynamics simulations, and PBPK modeling.

With a widespread distribution throughout the Northern Hemisphere and 11 genera, Pinaceae is the largest family of Gymnosperms in the world. Essential oils are an important chemotaxonomic marker for the species of this family, although the degree of chemical and biological investigation has not been the same for all genera. Essential oils from Abies and Cedrus (from the abietoid clade) or Pinus and Picea (from the pinoid clade) have been more extensively investigated with respect to their chemical composition and biological or pharmacological properties, including their antioxidant effects. Instead, essential oils from the other genera of the family have been less explored in this respect or even have not been investigated at all. This is a narrative review looking into the knowledge acquired up to date, the variability and limitations of the current methods used to estimate antioxidant effects, and multiple comparisons between EOs obtained from different genera, species, and plant parts, as well as potential applications and future directions of research and utilization of essential oils derived from Pinaceae species.

Background/Objectives: HMG-CoA reductase is an enzyme that regulates the initial stage of cholesterol synthesis, and its inhibitors are widely used in the treatment of cardiovascular diseases. Methods: We have created a set of quantitative structure-activity relationship (QSAR) models for human HMG-CoA reductase inhibitors using nested cross-validation as the primary validation method. To develop the QSAR models, we employed various machine learning regression algorithms, feature selection methods, and fingerprints or descriptor datasets. Results: We built and evaluated a total of 300 models, selecting 21 that demonstrated good performance (coefficient of determination, R² ≥ 0.70 or concordance correlation coefficient, CCC ≥ 0.85). Six of these top-performing models met both performance criteria and were used to construct five ensemble models. We identified the descriptors most important in explaining HMG-CoA inhibition for each of the six best-performing models. We used the top models to search through over 220,000 chemical compounds from a large database (ZINC 15) for potential new inhibitors. Only a small fraction (237 out of approximately 220,000 compounds) had reliable predictions with mean pIC₅₀ values ≥ 8 (IC₅₀ values ≤ 10 nM). Our svm-based ensemble model predicted IC₅₀ values < 10 nM for roughly 0.08% of the screened compounds. We have also illustrated the potential applications of these QSAR models in understanding the cholesterol-lowering activities of herbal extracts, such as those reported for an extract prepared from the Iris × germanica rhizome. Conclusions: Our QSAR models can accurately predict human HMG-CoA reductase inhibitors, having the potential to accelerate the discovery of novel cholesterol-lowering agents and may also be applied to understand the mechanisms underlying the reported cholesterol-lowering activities of herbal extracts.

The pharmaceutical industry faces significant challenges when promising drug candi-dates fail during development due to suboptimal ADME (absorption, distribution, metab-olism, excretion) properties or toxicity concerns. Natural compounds are subject to the same pharmacokinetic considerations. In silico approaches offer a compelling advantage: they eliminate the need for physical samples and laboratory facilities, while providing rapid and cost-effective alternatives to expensive and time-consuming experimental test-ing. Computational methods can often effectively address common challenges associated with natural compounds, such as chemical instability and poor solubility. Through a re-view of the relevant scientific literature, we present a comprehensive analysis of in silico methods and tools used for ADME prediction, specifically examining their application to natural compounds. Whereas we focus on identifying the predominant computational approaches applicable to natural compounds, these tools were developed for conventional drug discovery and are of general use. We examine an array of computational approaches for evaluating natural compounds, including fundamental methods like quantum me-chanics calculations, molecular docking, and pharmacophore modeling, as well as more complex techniques such as QSAR analysis, molecular dynamics simulations, and PBPK modeling.

HMG-CoA reductase is an enzyme that regulates the initial stage of cholesterol synthesis and its inhibitors are widely used in the treatment of cardiovascular diseases. Methods: We have created a set of quantitative structure-activity relationship (QSAR) models for human HMG-CoA reductase inhibitors using nested cross-validation as the primary validation method. To develop the QSAR models, we employed various machine learning regression algorithms, feature selection methods, and fingerprints or descriptor datasets. Results: We built and evaluated a total of 300 models, selecting 21 that demonstrated good performance (coefficient of determination, R2 ≥ 0.70 or concordance correlation coefficient, CCC ≥ 0.85). Six of these top-performing models met both performance criteria and were used to construct five ensemble models. We identified the descriptors most important in explaining HMG-CoA inhibition for each of the six best-performing models. We used the top models to search through over 220,000 chemical compounds from a large database (ZINC 15) for potential new inhibitors. Only a small fraction (237 out of approximately 220,000 compounds) had reliable predictions with mean pIC50 values ≥8 (IC50 values ≤10 nM). Our svm-based ensemble model predicted IC50 values <10 nM for roughly 0.08% of the screened compounds. We have also illustrated the potential applications of these QSAR models in understanding the cholesterol-lowering activities of herbal extracts, such as those reported for an extract prepared from the Iris × germanica rhizome. Conclusions: Our QSAR models can accurately predict human HMG-CoA reductase inhibitors, having the potential to accelerate the discovery of novel cholesterol-lowering agents and may also be applied to understand the mechanisms underlying the reported cholesterol-lowering activities of herbal extracts.

Background: Pharmaceutical innovation plays a vital role in advancing global health. This study evaluates the landscape of pharmaceutical innovation in the European Union (EU) over the 2011–2020 decade. Methods: A retrospective analysis was performed on new medicinal products containing new active substances (NAS) authorized between 2011 and 2020 through the centralized procedure. Products were categorized into first-in-class, advance-in-class, and addition-to-class. Trends in therapeutic areas (ATC codes), orphan designation, technology platforms, administration routes, and dosage forms were analyzed. Results: Across the decade, 357 new medicinal products received authorization. Of these, 56% were designated as first-in-class and 28% as advance-in-class, indicating that 84% of new products contributed substantive therapeutic innovation. Small molecules remained the predominant technology (63.5%), yet the decade also witnessed a pronounced expansion in monoclonal antibodies and the introduction of cell and gene therapies (Advanced Therapy Medicinal Products, ATMPs). The increased use of subcutaneous delivery systems, particularly for monoclonal antibodies, suggests a broader shift toward patient-centered administration routes. Conclusions: Between 2011 and 2020, pharmaceutical innovation in the European Union (EU) was characterized by a high rate of breakthrough innovations and a transition to more sophisticated biologic therapies. The EU could boost innovation further by refining expedited approval pathways (e.g., PRIME).