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12 Pharmaceutical Chemistry
1.4.2 Drug-like Property Optimization in Discovery
SAR is the new strategy introduced into drug discovery. The structures of compounds are
correlated to their activity mappings. SPR (structure-property relationship) allows
medicinal chemists to understand how structural modifications improve properties for
their scaffold. Thus, the established strategy of structure based design is supplemented
with the new strategy of “property based design” postulated by vanDe Waterbeemd
group, where the study and modification of structure is done to achieve property
improvement. There are many reasons for a drug discovery project team to strive toward
selecting leads with good drug-like properties and optimizing properties for their
compound series during drug discovery. Property optimization can be approached by
balancing with activity and selectivity optimization. The advantages of good drug-like
properties include the following:
Better planning, execution, and interpretation of discovery outcomes
Reduced discovery time lag from not having to fix property based problems at a later
time
Faster and more economical pharmaceutical development
Candidates with lower risk and higher future index
Longer patient life
Higher patient acceptance and compliance.
1.4.3 Rules for Rapid Detection of Drug-like Properties
There is need to apply some rules for evaluating the drug-like properties of the
pharmacophore. Rules are a set of guidelines for the structural properties of compounds
that have a higher probability of being well absorbed after oral dosing. The values of the
properties associated with rules are quickly counted from examination of the structure or
calculated using software (software are widely used now). These guidelines are neither
absolute, nor are they intended to form strict cut-off values for which property values are
drug-like and which are not. Nevertheless, they can be quite effective and efficient
techniques to get drug-like properties. For example, Lipinski’s rule, Veber rule, etc.
1.4.3.1 Lipinski’s Rules
Drug discovery is a major and important issue to design new drug entities which are
capable of penetrating different biological membranes effectively and rapidly enough to
allow effective concentrations to build up at the therapeutic targets. The structure and
physiochemical properties of the drug molecule obviously are of decisive significance, and
it is possible to establish the following empirical rules:
Some small and rather water-soluble substances pass in and out of cells through
water lined transmembrane pores.
Drug Discovery, Design and Development 13
Other polar agents are conducted into or out of cells by membrane associated and
energy consuming proteins. Polar nutrients that the cell requires, such as glucose
and many amino acids, fit into this category. More recently, drug resistance by cells
has been shown to be mediated in many cases by analogous protein importers and
exporters.
The blood brain barrier (BBB) normally is not easily permeable to neutral amino
acids. However, such compounds with sufficiently small differences between the
pK
values will have a relatively low I/U ratio indicating the ratio between ionised
a
(zwitterionic) and unionised molecules in solution. As an example, THIP (Gabaxadol)
has pKa values of 4.4 and 8.5 and a calculated I/U ratio of about 1000. Thus, 0.1% of
THIP in solution is unionised and this fraction permits THIP to penetrate the BBB
easily. Other neutral amino acids typically have I/U ratios around 500000 and thus
very low fractions of unionised molecules in solution, and such compounds normally
do not penetrate the brain after systemic administration. (data unmatched)
Molecules which are partially soluble in water and lipid can pass through the cell
membranes by passive diffusion and are driven in the direction of the lowest
concentration.
In cells lining in the intestinal tract, it is possible for molecules with these characteristics
to pass into the body through the cell membrane alone.
Finally, it is also possible for molecules with suitable water solubility, small size, and
compact shape to pass into the body between the cells. This last route is generally not
available for passage into the CNS, because the cells are packed closely together and
thus closing off these functions to form the BBB.
Whereas there is no guarantee and many exceptions, the majority of effective oral
drugs obey the Lipinski’s rule of five:
The substance should have a molecular weight of 500 or less.
It should have fewer than five H-bond donating (HBD) functionalities.
It should have fewer than ten H-bond accepting (HBA) functionalities.
The substance should have a calculated log P (clog P) between approximately –1 and
+5.
The Lipinski’s rule of five is thus not a rule comprising five paragraphs/points but
simply an empirical guide, wherein the number five occurs several times. The rule is a
helpful guide rather than a law of nature.
Examples of counting H-bond donors and acceptors are given in Table 1.2. For example,
an R-OH counts as both one H-bond donor (HBD) and one H-bond acceptor (HBA).
Although “violation” of one rule may not result in poor absorption, however the likelihood
of poor absorption increases with the number of rules broken and the extent to which they
are exceeded.
14 Pharmaceutical Chemistry
Table 1.2: Examples of counting H-bonds for Lipinski’s rules
Functional group H-bond donor H-bond acceptors
Hydroxyl 1 (OH) 1 (O)
Carboxylic acid 1 (OH) 2 (2-O)
-C(O)-NR
Primary amine 2 (NH2)1 (N)
Secondary amine 1 (OH) 1 (N)
Aldehyde 0 1 (O)
Ether 0 1 (O)
Ester 0 2 (O)
Nitrile 0 1 (N)
Pyridine 0 1 (N)
2
02 (N-O)
H-bonds increase solubility of a drug in water and must be broken in order for the
compound to permeate the lipid bilayer membrane. Thus, an increasing number of
H-bonds reduce partitioning from the aqueous phase into the lipid bilayer membrane for
permeation by passive diffusion. Molecular weight (MW) is related to the size of the
molecule. As the molecular size increases, a larger cavity is formed in water in order to
solubilise the compound and the solubility decreases. High MW of the drug reduces its
concentration at the surface of the intestinal epithelium resulting in low absorption.
Increase in size also impedes passive diffusion through the tightly packed aliphatic side
chains of the bilayer membrane. Increasing Log P also decreases aqueous solubility which
again reduces absorption. Finally, membrane transporters can either enhance or reduce
compound absorption by either active uptake, transport or efflux, respectively. Thus,
transporters can have a strong impact on increasing or decreasing the absorption.
1.4.3.2 Veber Rules
Veber et al., studied the structural properties that increase oral bioavailability in rats. They
concluded that molecular flexibility, polar surface area (PSA) and H-bond count are
important determinants of oral bioavailability. Rotatable bonds can be counted manually
or by using software. PSA is calculated using software and is closely related to H-bonding.
Veber rules for good oral bioavailability in rats are as follows:
d10 rotatable bonds
2
d140 Å
PSA, or d12 total hydrogen bonds (acceptors plus donors)
1.4.4 Application of Rules for Compound Assessment
Rules are typically used for the following purposes:
Anticipating the drug-like properties of potential compounds when planning
synthesis.
Using the drug-like properties of “hits” from HTS as one of the important selection
criteria.
Drug Discovery, Design and Development 15
Evaluating the drug-like properties of compounds to be procured from vendors.
An example of the counting and calculation of rules for the compound doxorubicin
is shown in Fig. 1.9. Doxorubicin has a very low oral bioavailability (approx. 5%), as
would be anticipated from the structural properties covered by the rules. Figure
1.10, shows an example of how rules could help anticipate absorption in a drug
discovery project. Structural modifications of the compound on the left were made
to optimize the activity, resulting into the compound on the right. Unfortunately, the
binding-optimized compound had poor absorption after oral dosing, as could be
predicted from the structural properties prior to synthesis.
Exceeding the rules often reduces absorption after oral dosing. Poor absorption
properties result in low bioavailability or the need for dosing via an alternate route, either
of which limits the potential scope of the drug moiety.
O
H3CO
Fig. 1.9: Doxorubicin: Analysis using Lipinski’s and Veber rules (Guidelines are exceeded for all rules except the ClogP)
A, Potency = 1 Mm
HBD = 0, HBA = 3, MW = 369,
LogP = 5.7, PSA = 17,
Fig. 1.10: Comparative structural optimization of A & B: Activity against neuropeptide YY1 antagonist (Modified
compound B demonstrated 2000-fold increase in potency while it had very poor absorption properties after oral
O
N
O
N
Rotatable bonds = 6
O OH
OH
OH
Me
OH O
dosing, as anticipated from the structural rules.)
OH
O
NH
2
Cl
Lipinski’s rule
' H-bond donors = 7
' MW = 543
' ClogP = –1.7
' H-bond acceptor = 12
N
N
O
O Cl
N
NH
Veber rule
' Rotatable bonds = 11
' PSA = 206
'Total H-bonds 19
B, Potency = 1nM
Poor oral absorption
HBD = 1, HBA = 6, MW
LogP = 7.3, PSA = 50,
Rotatable bonds = 14,
Total HB = 6
= 591,
1.5 MODERN APPROACH OF RESEARCH AND DEVELOPMENT STRATEGIES
Recent concerns about escalating drug prices and rising health-care expenditure have
sparked considerable interest in discovering new drugs with cost-effective tests, patients
savvy and how well these processes serve the interests of living things. The complex
economic forces that govern the drug discovery processes are not widely understood.
16 Pharmaceutical Chemistry
Thus, it describes the current state of pharmaceutical research and development (R&D),
analyzes the forces that influence it and considers how well markets are working to deliver
the new drugs.
Much of the public interest in pharmaceutical R&D concerns the relationship between
drug prices, drug firms’ costs, the pace and direction of innovation. Average prices of new
drug products have been rising proportionately faster than the rate of inflation and annual
R&D spending has grown. Nevertheless, introduction of innovative drugs have been slow.
At the same time, drug companies have been able to charge high retail prices for new
drugs that are only incrementally different from the older drugs the prices of which have
fallen.
Pharmaceutical markets, however, are extremely complex in many respects. Large
public sector investments in basic biomedical R&D influence private companies’ choices
about what to work on and how intensively to invest in research and development. The
returns on private sector R&D investment are attractive on an average, but they vary
considerably from one drug to the next.
It is, however, pertinent to mention here that a proprietary new chemical entities status,
position and recognition in an absolute must not only ensure marketing exclusively but
also justify the huge investment in the ensuing research and development process thereby
making medicinal chemistry a more or less core element of the entire ‘drug discovery
process’.
Interestingly, the ‘drug discovery process’ may be categorised into four distinct heads,
namely:
Target identification and selection
Target optimization
Lead identification
Lead optimization
In short, the qualified success in the ‘drug discovery process’ predominantly revolves
around the following cardinal factors as follows:
Articulated project management processes
Prioritization
Well defined aims and objectives
Company organization(s) and culture
Resourcing modus operandi
Prompt decision making factors.
1.5.1 Therapeutic Targets: Identification and Validation
Target discovery, which comprises identification and validation of disease modifying
targets, is an essential early step in the drug discovery pipeline. A number of approaches
to target discovery have been described in recent years. These approaches and models
Drug Discovery, Design and Development 17
incorporate recent technological advances, such as genomics, proteomics, transcriptomics—
like small interfering RNAs (siRNAs) and metabolomics—studying the active metabolite
of the drug administered.
Various techniques applied in target identification and validation can be grouped into
two broad target discovery strategies: 1. “Molecular” and 2. “System” approaches (Fig.
1.11). In practice, however, both are used in varying proportions within different therapeutic
areas. The “systems approach” should not be confused with the recent emergence of
“system biology” which is an attempt to construct models that explain biological responses
using the plethora of information being produced from the molecular sciences.
The “molecular approach” is focused on the cells implicated in the diseases and uses
clinical samples and cell models. The molecular approach has been driven by the enormous
experimental successes of molecular biology and in particular genomics. In terms of target
classes, the molecular approach is more likely to identify intracellular targets such as
structural and metabolic proteins that have been most extensively deployed in oncology.
In recent years, there has been a significant shift towards the molecular approach in an
attempt to identify new targets through an understanding of the cellular mechanisms
underlying disease phenotypes of interest.
Overview
Path A:
Molecular
approaches
Path B:
Systems
approaches
Disease model
Clinical
samples
Cell
models
Patients
Animal models
Fig. 1.11: Techniques applied in target identification and validation
Target identification
Genomics,
proteomics,
genetic association
Forward genetics
Clinical sciences
Forward genetics
reverse genetics
Target validation Drug discovery
Disease tissue
expression
Cell models:
mRNA KO, protein
overexpression
Animal models:
(conditional) KO,
transgenic mice
Drug discovery:
High throughput
screening of
compound
libraries
Structure-based
drug design
The “systems approach” is geared towards target discovery through the study of
diseases in the whole organism. In general, this information is derived from the clinical
18 Pharmaceutical Chemistry
sciences and in vivo animal studies in physiology, pathology and epidemiology. The
systems approach has been traditionally the main target discovery strategy and this
remains the case for many diseases including obesity, arteriosclerosis, heart failure, stroke,
behavioural disorders, neurodegenerative diseases and hypertension, in which the relevant
phenotype can only be detected at the organism level. For these crucial reasons, the
majority of current drugs are identified through this strategy and includes those that act
against the disease phenotype and intracellular/extracellular targets. Interestingly,
because many of these drugs are directed against targets which were identified from
physiological studies rather than being directly implicated in the disease mechanism and
also they would probably not have been identified by the molecular approach. For example,
although changes in ǃ
-adrenoreceptor expression/activity in airway smooth muscle have
2
not been implicated in the mechanism of allergen hyper reactivity that produces airway
contraction in asthma; these symptoms are commonly treated with ǃ
-agonist.
2
The incidence of many chronic diseases is strongly correlated with age and such diseases
are thought to be influenced by both genomic and environmental factors. The overall
contribution of genomic factors is still unknown although it is believed that many diseases
are influenced by the presence of susceptibility genes. With the exception of smoking the
role of environmental factors is ambiguous, although a number of studies have indicated
the importance of infection/inflammation and diet in diseases such as arteriosclerosis,
CNS diseases and cancer.
In undertaking target discovery, one would ideally perform clinical studies and obtain
cell/tissue samples using normal and diseased human patients. In reality, this is usually
unethical and/or impractical, which means we must rely on cellular and/or animal
models. However, such models often suffer from a number of significant problems which
make them poor predictors of human disease. In the case of cell models, the central problem
lies in simulating the complexity of the in vivo biological interactions particularly as many
of these are unknown. This problem of complexity makes it increasingly difficult to predict
the role of protein as one proceeds from the cellular level to tissue and organism. In
addition, the use of immortalized cell lines to overcome the problems of availability
prompts the questions regarding their biochemical similarity with primary cells. To
overcome the problems of complexity, we often use animal models. However, although
these models may reproduce a particular disease phenotype, genomic differences (related
to species and strain) and the difficulty of identifying and replicating the long-term
environmental influences.
1.5.2 The Drug Development Process: An Outline
The stages through which a drug discovery/development project progress from inception
to marketing and beyond are illustrated below. From this outline, the complexity of the
task of finding new therapeutic agents is evident.
1. Identification of target disease, establishment of a multidisciplinary research team,
selection of a promising approach and decision on a sufficient budget. Initiation of
Drug Discovery, Design and Development 19
chemistry, which normally involves synthesis based on available chemicals or
collection of natural product sources. Start of pharmacology, includes suitable
screening methods and choice of receptor binding and/or enzymatic assays.
2. Confirmation of potential utility of initial class(es) of compounds in animal, focusing
on potency, selectivity, and apparent toxicity.
3. Analogue syntheses of the most active compounds, planned after careful examination
of literature and patents. More elaborated pharmacology in order to elucidate mode
of action, efficacy, acute and chronic toxicity, and genotoxicity. Studies of ADME
characteristics. Planning of large-scale synthesis and initiation of formulation
studies. Application for patent protection.
The early project phases, which typically last 4-5 years, are followed by time and
resources demanding clinical, regulatory and marketing phases, which normally last
about 10 years.
1. Phase I clinical studies, which include safety, dosage, and blood level studies.
2. Phase II clinical studies focusing on efficacy and side effects.
3. Phase III clinical studies involve studies of range of efficacy and long-term and rare
side effects.
4. Regulatory review.
5. Marketing and Phase IV clinical studies focusing on long-term safety.
6. Large-scale production.
7. Distribution, advertisement, education of marketing and information personnel.
Completing these project stages from initiation to successful therapeutic application
after approval, the patent protection expires, normally after 17-25 years, and generic
competition becomes a reality. This outline of a drug development project illustrates that,
at best, it takes many years to introduce a new therapeutic agent, and it must be kept in
mind that maximum number of projects are terminated not only before marketing but at
advanced stages of clinical studies also.
1.5.3 Structure-Activity Relationships (SARs)
Structure-activity relationships (SARs) are the traditional practices of medicinal chemistry
which attempt to modify the effect or the potency (e.g., activity) of bioactive chemical
compounds by modifying their chemical structure. Medicinal chemists use the techniques
of chemical synthesis to insert new chemical groups into the biomedical compound and
test the modifications for their biological effects. SARs are the relations between the
molecular structure and biological or physicochemical activity of chemicals. The SAR
paradox refers to the fact that it is not the case that all similar molecules have similar
activities.
The rationale behind SAR is that the structure of the chemical implicitly determines its
physical and biological properties and reactivity, which in interaction with a biological
system, determines its biological/toxicological properties. Therefore, the science of SAR is
20 Pharmaceutical Chemistry
in relating the structure to activity, i.e., identifying the key aspects of structure, pertaining
to the molecular event(s) in the mechanism of action for the chemical or biological actions
of interest. The direct structural similarity is much exploited one, however, it is often
unsuccessful (does not always imply similarity in activity) mainly because key aspects of
the structure related to activity are not known.
1.5.3.1 Structure-Activity Relationship: Definitions
1. Structure-activity relationship is the analysis of the dependence of the biological
effects of a chemical upon its molecular structure that produces a structure-activity
relationship. Molecular structure and biological activity are correlated by observing
the results of systematic structural modification on the defined biological endpoints.
2. SAR is the relationship between the chemical or three-dimensional structure of a
molecule and its biological activity.
3. SAR is the relationship between chemical structure and pharmacological activity
for a series of compounds.
4. The analysis of SAR enables the determination of the chemical groups responsible
for evoking a target biological effect in the organism.
5. This allows modification of the effect or the potency of a bioactive compound
(typically a drug) by changing its chemical structure.
6. Medicinal chemists use the techniques of chemical synthesis & computational drug
design to insert new chemical groups into the biomedical compounds and test the
modifications for their biological effects.
7. Using SAR we can conclude the effect of a drug or toxic chemical on an animal,
plant or the environment—can be related to its molecular structure. This type of
relationship may be assessed by considering a series of molecules and making
gradual changes to them, noting the effect upon their biological activity of each
change. Alternatively, it is possible to assess a large body of toxicity data using
intelligent tools such as neural networks to try to establish a relationship. Ideally,
such relationships can be formulated as quantitative structure activity relationships
(QSARs), in which some degree of predictive capability is present.
8. The relationship between chemical structure and pharmacological activity for a
series of compounds. Compounds are often classed together because they have
structural characteristics in common including shape, size, stereochemical
arrangement, and distribution of functional groups. Other factors contributing to
SAR include chemical reactivity, electronic effects, resonance and inductive effects.
9. This method was refined to build mathematical relationships between the chemical
structure and the biological activity, known as quantitative structure-activity
relationships (QSARs).
10. Structure-property correlations refer to all statistical mathematical methods used to
correlate any structural property to any other property (intrinsic, chemical or
biological), using statistical regression and pattern recognition.
Drug Discovery, Design and Development 21
1.5.3.2 The SAR Paradox
The basic assumption for all molecule based hypotheses is that similar molecules
have similar activities.
This principle is the basis of SAR. The underlying problem is how to define a small
difference at molecular level as each kind of activity (e.g., reaction ability,
biotransformation ability, solubility, target activity and so on,) might depend on
another difference.
In general, one is more interested in finding strong trends.
Created hypotheses usually rely on a finite number of chemical data.
The SAR paradox refers to the fact that it is not the case that all similar molecules
have similar activities.
1.5.4 Quantitative Structure-Activity Relationships (QSAR)
SAR enables the identification and determination of the chemical groups responsible for
evoking a target biological effect in an organism. This method was later refined to build
mathematical relationships between a chemical structure and its biological activity which
is known as quantitative structure-activity relationships (QSAR). Some scientists relate
QSARs as a special case of SARs (when relationships become quantified), while others
exploit notions of chemical similarity to predict the activity of chemicals. The latest is also
known as “read-across” approach, stating that the unknown property of a compound is decided to
be the same as the known property of another compound if the two compounds are sufficiently
similar.
Attempting to quantify SARs allows drug designers to more rapidly identify likely drug
molecules or an attempt to correlate structural/property descriptors of compounds with
activities. These physicochemical descriptors; including parameters to account for
hydrophobicity, topology, electronic properties and steric effects; are determined
empirically or, more recently, by computational methods. While it is true that such methods
have a significant level of uncertainty attached to them, the use of quantitative data allied
to the chemists’ knowledge of the behaviour of functional groups is providing an
increasingly valuable tool.
Surprisingly, QSAR modelling techniques date from well before the era of computeraided drug design. Early methods involved characterise the groups present in a potential
drug molecule by parameters such as their lipophilicity. This is measured by calculating
the partition coefficient (P
compound in octan-1-ol and water at equilibrium with one another as follows:
A high partition coefficient indicates high lipophilicity and low hydrophilicity. The
overall contribution of these parameters to the likely activity of the molecule could then be
estimated by mathematical manipulation. Activities used in QSAR include chemical
), which is calculated by knowing the concentration of the
i
Concentration of compound in octan-1-ol
P
=
i
Concentration of compound in water