Design of Novel Antihistamines and Nonsteroidal Anti-inflammatory Drugs (NSAIDs) | Chapter: 1 | Modern Advances in Pharmaceutical Research Vol. 1
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 testand 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 ofoxygen
& 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.
Biography of author(s)
Ronald Bartzatt
Durham Science Center, University of Nebraska, 6001 Dodge
Street, Omaha, Nebraska 68182, USA.
Read full article: http://bp.bookpi.org/index.php/bpi/catalog/view/47/229/394-1
View volume:https://doi.org/10.9734/bpi/mapr/v1
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