DƯỢC LÍ Goodman & Gilman's The Pharmacological Basis of Therapeutics 12th, 2010

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SECTION I
GENERAL PRINCIPLES
the target for the fibrate class of hyperlipidemic drugs, including the
widely prescribed gemfibrozil and fenofibrate. While activation of
PPARα results in induction of target genes encoding fatty acid
metabolizing enzymes that result in lowering of serum triglycerides,
it also induces CYP4 enzymes that carry out the oxidation of fatty
acids and drugs with fatty acid-containing side chains, such as
leukotriene and arachidonic acid analogs. PPARγ is the target for the
thiazolidinedione class of anti-type 2 diabetic drugs including
rosiglitazone and pioglitazone. PPARγ does not induce xenobiotic
metabolism.
The UGT genes, in particular UGT1A1, are inducible via a
host of transcriptional activation pathways, including AHR, Nrf2
(nuclear factor erythroid 2 related factor 2, a transcriptional regulator
of cytoprotective genes that is induced by an antioxidant response),
PXR, CAR, and PPARα. Since the UGTs are abundant in the GI
track and liver, regulation of the UGTs by drug-induced activation of
these receptors would be expected to affect the pharmacokinetic
parameters of many orally administered therapeutics.
Role of Drug Metabolism in the Drug Development
Process. There are two key elements associated with
successful drug development: efficacy and safety. Both
depend on drug metabolism. It is necessary to determine
which enzymes metabolize a potential new drug candidate
in order to predict whether the compound may
cause drug-drug interactions or be susceptible to marked
inter-individual variation in metabolism due to genetic
polymorphisms. Computational chemical systems biology
and metabolomic approaches could enhance this
process.
Historically, drug candidates have been administered to
rodents at doses well above the human target dose in order to predict
acute toxicity. For drug candidates to be used chronically in humans,
such as for lowering serum triglycerides and cholesterol or for treatment
of type 2 diabetes, long-term carcinogenicity studies are carried
out in rodent models. For determination of metabolism, the compound
is subjected to analysis by human liver cells or extracts from
these cells that contain the drug-metabolizing enzymes. Such studies
determine how humans will metabolize a particular drug, and to a
limited extent, predict the rate of metabolism. If a CYP is involved,
a panel of recombinant CYPs can be used to determine which CYP
predominates in the metabolism of the drug. If a single CYP, such as
CYP3A4, is found to be the sole CYP that metabolizes a drug candidate,
then a decision can be made about the likelihood of drug interactions.
Interactions become a problem when multiple drugs are
simultaneously administered, e.g. in elderly patients, who on a daily
basis may take prescribed anti-inflammatory drugs, one or two
cholesterol-lowering drugs, several classes of blood pressure medications,
a gastric acid suppressant, an anticoagulant, and a number of
over-the-counter medications. Ideally, the best drug candidate would
be metabolized by several CYPs so that variability in expression levels
of one CYP or drug-drug interactions would not alter its metabolism
and pharmacokinetics.
Similar studies can be carried out with phase 2 enzymes and
drug transporters in order to predict the metabolic fate of a drug.
In addition to the use of recombinant human xenobiotic-metabolizing
enzymes in predicting drug metabolism, human receptor-based
(PXR and CAR) systems or cell lines expressing these receptors are
used to determine whether a particular drug candidate could be a ligand
or activator of PXR, CAR, or PPARα. For example, a drug that
activates PXR may result in rapid clearance of other drugs that are
CYP3A4 substrates, thus decreasing their bioavailability and
efficacy.
Computer-based computational (in silico) prediction of
drug metabolism is a not-so-distant prospect, as is the structural
modeling of the regulation of CYP expression via activation of
nuclear receptors. Currently, chemical systems biology
approaches are being applied on a proteome-wide scale to discover
novel drug leads (Kinnings et al., 2009). The large size of
the CYP active sites, which permits them to metabolize many different
compounds, also renders them difficult to model.
However, with refinement of structures and more powerful modeling
software, in silico drug metabolism may become a useful
adjunct to experimentation.
Determining the potential for a drug candidate to produce
toxicity in pre-clinical studies is vital and routine in the drug
development process. Historically, this is typically done by administering
the drug candidate to rodents at escalating doses, usually
above the predicted therapeutic dose that will be used in humans.
Signs of toxicity are monitored and organ damage assessed by
postmortem examination. This process is not high throughput and
can be a bottleneck in the drug development process of lead compound
optimization. A new technology of high-throughput screening
for biomarkers of toxicity is being adopted for drug development
using metabolomics. Metabolomics is the systematic
identification and quantification of all metabolites in a given organism
or biological sample. Analytical platforms such as 1 H-NMR
and liquid or gas chromatography coupled to mass spectrometry, in
conjunction with chemometric and multivariate data analysis, allow
the simultaneous determination and comparison of thousands of
chemicals in biological fluids such as serum and urine, as well as
the chemical constituents of cells and tissues. This technology can
be used to monitor for drug toxicity in whole animal systems during
pre-clinical drug development and can obviate the need for
time-consuming and expensive postmortem analyses on thousands
of animals. Using metabolomics, test animals can be analyzed for
the presence of one or more metabolites in urine that correlate with
drug efficacy or toxicity. Urine metabolites that are fingerprints for
liver, kidney and CNS toxicity have been identified using known
chemical toxicants. Metabolic fingerprints of specific compounds
that are elevated in urine can be used to determine, in dose escalation
studies, whether a particular drug causes toxicity, and can also
be employed in early clinical trials to monitor for potential toxicities.
Metabolomics can be used to find biomarkers for drug efficacy
and toxicity that can be of value in clinical trails to identify responders
and nonresponders. Drug metabolism can be studied in whole
animal model systems and in humans to determine the metabolites
of a drug or indicate the presence of a polymorphism in drug
metabolism that might signal an adverse clinical outcome. Finally,
biomarkers developed from experimental metabolomics could
eventually be developed for routine monitoring of patients for signs
of drug toxicity.