Design of Novel Antihistamines and Nonsteroidal Anti-inflammatory Drugs (NSAIDs)

Introduction: The group of drugs referred to as non-steroidal anti-inflammatory agents (NSAIDs) are
applied in the treatment of fever, pain, acute and chronic inflammatory conditions. Generally, NSAIDs
are highly bound to plasma proteins such as albumin, which decreases their body distribution to
levels, considered low (i.e. as low as or lower than 0.2 Liter/kg).
Aims: To determine the molecular properties of common antihistamines and non-steroidal antiinflammatory
agents (NSAIDs). To identify interrelationships among these two groups of drugs
utilizing pattern recognition methods and statistical analysis.
Study Design: After determination of molecular properties, values thereof are examined using pattern
recognition methods and other numerical analysis for underlying relationships and similarities.
Place and Duration of Study: Durham Science Center, University of Nebraska, Omaha, Nebraska
from September 2016 to January 2017.
Methodology: Thirty compounds were identified as antihistamines and 27 compounds identified as
NSAIDs. Properties such as Log P, molecular weight, polar surface area, etc. are determined.
Molecular properties are compared applying methods such as K-means cluster analysis, nearest
neighbor joining, box plots, and statistical analysis in order to determine trends and underlying
relationships. Pattern recognition techniques allow elucidation of underlying similarities.
Results: The molecular properties of all 57 drugs are tabulated for comparison and numerical
analysis. Evaluation by Kruskal-Wallis test and one-way ANOVA indicated that antihistamines and
NSAIDs’ values of Log P have equal medians and equal means. However, values of polar surface
area (PSA) and number of rotatable bonds for these two groups do not have equal means and
medians. Box plots indicated that Log P, PSA, and molecular weight values have significant overlap in
range. Neighbor-joining method showed which drugs are most similar to each other. K-means cluster
analysis also divided these 57 drugs into six groups of highest similarity. Principal coordinates
analysis (PCoA) with 95% ellipses indicated all but four of the drugs fall within a 95% confidence
region. Multiple regression analysis generated mathematical relationship for prediction of new drugs.
Conclusion: These two groups of drugs show compelling similarities. PCoA showed all but four of 57
drugs come within a 95% confidence ellipsis. Neighbor joining and K-means cluster analysis showed
drugs having similarities between the two groups. Antihistamines and NSAIDs are two groups of
drugs highly important for public health. A comparison of 30 antihistamines to 27 NSAIDs showed
important similarities useful for design of novel drug structures. One-way ANOVA and Kurskal-Wallis
test showed that means and medians of Log P and number of oxygen & nitrogen atoms of these two
groups are equal. Properties NSAIDs showed high level of consistent values, with no outliers for Log
P, polar surface area, molecular weight, molecular volume, and numbers of –OH, -NHn, rotatable
bonds, and atoms. However, antihistamines showed outliers in all properties except Log P and
number of rotatable bonds. Multiple regression produced algorithms for both groups accounting for
over 93% of variance in molecular weight. Box plots showed substantial overlap of values for the two
groups of drugs for molecular weight, polar surface area, and Log P. K-means cluster analysis
showed that members of antihistamines are most similar to members of NSAIDs. Similarity among
members of the two groups is visualized in neighbor joining tree cluster analysis.