Research trends in different pharmaceutical areas: Natural product chemistry Imtiaj Hossain Chowdhury B’Pharm (Jahangirnagar University), M’Pharm (Jahangirnagar University) Master’s in Public Health (American International University Bangladesh)

1. Research trends in different pharmaceutical areas: Natural product
chemistry
Imtiaj Hossain Chowdhury
B’Pharm (Jahangirnagar University), M’Pharm (Jahangirnagar University)
Master’s in Public Health (American International University Bangladesh)
Top-Down Approaches:
Model-based drug development paradigm is a relatively new concept introduced to increase the
pharmaceutical research and development productivity. Its main aim is to shift compound attrition
from late clinical development to earlier stages, by providing more robust, data-based and clear
criteria for the go/no-go decisions (Tylutki et al., 2016). One of the emerging approaches to
discovering new drugs is called top-down approach. The top-down approach assumes that one can
discover drugs by looking at their effects on biological systems, by gathering enough data about
them without understanding their inner lives, by generating numbers through trial and error, by
listening to what those numbers are whispering in his/her ear. Top-down approach assumes
ignorance. Since human beings have been ignorant for most of their history, for most of the
recorded history of drug discovery they have pursued the top-down approach. When you don't
know what works, you try things out randomly. The Central Americans found out by accident that
chewing the bark of the Cinchona plant relieved them of the afflictions of malaria. The top-down
approach may seem crude and primitive. The Top-down approach is fundamentally steeped in data
analysis and takes advantage of new technology that can measure umpteen effects of drugs on
biological systems, greatly improved computing power and hardware to analyze these effects, and
refined statistical techniques that can separate signal from noise and find trends. The top-down
approach is today characterized mainly by phenotypic screening and machine learning. Phenotypic
screening involves simply throwing a drug at a cell, organ or animal and observing its effects. In
its primitive form it was used to discover many of today's important drugs; in the field of anxiety
medicine for instance, new drugs were discovered by giving them to mice and simply observing
how much fear the mice exhibited toward cats (Wavefunction, 2017). Machine Learning is a
branch of Artificial Intelligence (AI) that aims to develop and apply computer algorithms that have
been used in the pharmaceutical industry for the prediction of new molecular characteristics,

2. biological activities, interactions and adverse effects of drugs. Some examples of these methods
are Naive Bayes, Support Vector Machines, Random Forest and, more recently, Deep Neural
Networks (Carracedo-Reboredo, 2021).
Bottom-Up Approaches
The bottom-up approach to systems biology forms detailed models from subunits of data to
simulate whole systems under different physiological conditions. The bottom-up approach has the
advantage of analyzing base units in detail before being integrated into a larger system. Within the
field of systems biology, this approach has produced tissue-specific simulations for modeling
responses to condition variability. By applying a bottom-up approach to pharmacology, the
possibility of simulated drug safety assessments is being made into a reality. The approach starts
with draft reconstruction, where organism specific data such as genomics and metabolomics are
collated from databases (Stoakes, 2018).
One such example is the formation of HepatoNet1 based modeling; a reconstruction of the human
liver from using the bottom-up approach, which tests components of liver function under various
conditions (Stoakes, 2018).
Robust And High Throughput Screening
High-throughput screening (HTS) is one of the newest techniques used in drug design and may be
applied in biological and chemical sciences. This method, due to utilization of robots, detectors
and software that regulate the whole process, enables a series of analyses of chemical compounds
to be conducted in a short time and the affinity of biological structures which is often related to
toxicity to be defined. It is basically a process of screening and assaying a large number of
biological modulators and effectors against selected and specific targets. HTS assays are used for
screening of different types of libraries, including combinatorial chemistry, genomics, protein, and
peptide libraries. The main goal of the HTS technique is to accelerate drug discovery by screening
large compound libraries at a rate that may exceed a few thousand compounds per day or per week.
It is of vital importance, because parallel and combinatorial chemical synthesis generates a vast
number of novel compounds. High-throughput screening methods are also used to characterize
metabolic, pharmacokinetic and toxicological data about new drugs (Szymański et al., 2012). As
an example, the high-throughput screening process successfully identified a potent pan-SRC

3. kinase inhibitor now known as ‘Dasatinib, BMS-354825’ for the biological target of diabetes. The
high-throughput screening processes involve various detection methods such as robotics or plate
readers and corresponding software to process and analyze the data obtained.
The high-throughput screening HTS assays helps to screen various types of libraries such as
genomics, protein, combinatorial chemistry, and peptide libraries. The high-throughput screening
HTS and assay method includes various steps such as preparation of reagents, target identification,
compound management, assay development, and high throughput library screening, which are
performed with extreme care and precision. The detail steps are as follows:
Firstly, targets are selected. There are presently around 500 targets that are being utilized by
various companies. Among these targets, cell membranes receptors, mostly G-protein coupled
receptors are commonly used and comprise the largest group with 45% of the total, followed by
Enzymes (28%), hormones (11%), unknowns (7%), ion-channels (5%), nuclear receptors (2%),
and DNA (2%). Off late pharmaceutical companies are interested to analyze compounds that
interfere or modulate the function of GPCRs (Bokhari, 2021).

4. Figure: Major types of high-throughput data and their key information relevant to drug
discovery. Metabolomic data belong to cheminformatics and are not included (Xia, 2017).
Bioinformatics Tools
Bioinformatics is the combination of health information, data and knowledge. Bioinformatics as it
relates to medicine involves the processing of the genetic information with the hope of generating
the genetic basis of health and disease that could result in the efficient discovery of tailored and
targeted drugs (Mbah, 2019). It uses computational techniques and tools to analyze the enormous
biological data bases. The diseases such as metabolic disorders, urea cycle disorders, inborn errors
and path-aligner can be identified at the early stage using various bioinformatics computational
tools. These tools are used to process genetics and proteomics data and compare with health care
data (Majhi, 2019). As an interface between modern biology and informatics, it entails discovery,

5. development and implementation of computational algorithms and software tools in an effort to
facilitate an understanding of the biological processes (Mbah, 2019).
Bioinformatics is of importance to Pharmacy (Pharmaceutical bioinformatics) in the areas of (i)
drug discovery, designing and development, (ii) product/formulation designing, (iii)
Pharmacokinetics and pharmacology. Pharmaceutical bioinformatics deals with scientific area of
computer-based technologies and informatics, computational methods for mapping processes of
the cells (genetic information) and understanding how to use these properties to effectively
discover and develop novel drugs. The novel drugs could be tailored or targeted drugs. Target
drugs are drugs designed specifically to act on particular genes and their corresponding protein
identified to be responsible for certain disease conditions. While tailored drugs refer to drugs
designed to handle the needs of a specified genetic sub-group of the entire population. The
discovery and development process involve the employment of computer-aided drug design
(CADD) methods. CADD methods are dependent on bioinformatics tools, applications and
databases. The methods entail building three dimensional (3-D) virtual compound libraries
(databases) for in silico screening (virtual screening) by docking the compounds against validated
drug targets, followed by judicious selection of virtual hits possessing appropriate
physicochemical properties to be screened for biological activity. Some libraries consist of
compounds with activities against several diseases, e.g., the ZINC database while others are
activity focused libraries. The library is usually filtered to eliminate irrelevant molecules through
a concept referred to as 'rapid elimination of swill' (REOS). REOS aids to identify molecules with
poor absorption, distribution, metabolism, elimination and toxicology (ADME/T) properties.
Thereafter, virtual screening is carried out by docking the "filtered out" library (or dataset) against
validated drug targets in order to identify promising hit compounds, which are then subjected to
biological activity assays (Mbah, 2019).
References
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