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 anti-inflammatory 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.

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