Poisons and Drugs by qingyunliuliu


									Poisons and Drugs

 Prof. Monzir S. Abdel-Latif
   Chemistry Department
 Islamic University of Gaza

Essential Substances

                 Therapeutic Index
   The Therapeutic Index of a drug is the ratio of the toxic to
    the therapeutic dose. Drugs with a low therapeutic index may
    only require a small increase in dose to produce toxic effects.

   TI = TD50/ED50

ED50 is the therapeutically effective dose while TD50 is the toxic
  dose, both in 50% of population

   The therapeutic index for diazepam is somewhat forgiving,
    about = 100. Other drugs, however are much less (such as
    Digoxin, which has an index of 2 or 3(.
   Sometimes the term safety ratio is used instead,
    particularly when referring to psychoactive drugs
    used for non-therapeutic (i.e. nonmedical) purposes.
    In such cases, the "effective" dose is that which
    produces the desired effect, which can vary and can
    be greater or less than the therapeutically effective
   ED50 in this case is the dose that brings the desired
    effect, while TD50 has the same definition as before.

    Non-animal Alternative Methods
   In vitro methods and quantitative structure-activity
    relationship ((QSAR) models for the prediction of
    acute systemic toxicity have been reviewed in several
    recent workshops and articles.
   A non-animal replacement method for acute systemic
    toxicity will have to provide information on many
    complex biological processes, including
    toxicokinetics and organ toxicity.

       Spectrum of Toxic Effects
We have seen earlier that toxic effects can be :

1.  Local or systemic
2.  Reversible or irreversible
3.  Immediate or delayed
A good example of delayed toxic effects was observed in cases
    where pregnant women were given diethylstilbesterol (DES)
    to prevent miscarriages. However, daughters of these
    mothers developed vaginal cancer after 15-20 years, while
    males developed prostate cancer and reproductive
    dysfunction. Therefore, long-term studies are essential not
    only on subjects taking the drug but on fetus and offspring.

 Toxicity versus Allergic Reactions
Allergic reaction (also known as hypersensitivity and
  sensitization reaction) to a toxicant results from previous
  sensitization to that toxicant or a chemically similar one. The
  chemical acts as a hapten and combines with an endogenous
  protein to form an antigen, which in turn induces the formation
  of antibodies. A subsequent exposure to the chemical will
  result in an antigen–antibody interaction, which provokes the
  typical manifestations of allergy. Thus, this reaction is
  different from the usual toxic effects, first because a previous
  exposure is required, and second because a typical sigmoid
  dose–response curve is usually not demonstrable with allergic

Receptors are located across plasma membrane,
 or in cytosol, or nucleus, and serve to transmit
 physical or chemical signals to the cell. There
 are many types of receptors serving a variety
 of functions. Some of them are known to be
 affected by toxicants.

              Some Receptors
Neurotransmitter receptors include the cholinergic
  (nicotinic, located in ganglia and skeletal muscles,
  and muscarinic in smooth muscles and brain), - and -
  adrenergic, dopamine, opiates, and histamine (H1 and
  H2) receptors.
Hormone receptors are for molecules like insulin,
  cortisone, thyrotropin, estrogen, progesterone,
  angiotensin, glucagon, prostaglandin, and others.
Certain chemicals such as antidepressants and antitumor
  agents bind with specific macromolecules that may be
  considered as “drug receptors.”
 Structure and Signal Transduction
The various functional categories of receptors described
    above exert their biological effects upon binding
    with an appropriate ligand, which may be an
    endogenous or exogenous substance. These effects
    are preceded by a series of biochemical activities,
    the signaling, which vary according to the structural
    characteristics of the receptor. Structurally they may
    be placed in four classes of receptors. These are:
(1) G-protein coupled receptors,
(2) ligand-gated ion channels,

(3) voltage-gated channels, and
(4) intracellular receptors.

Enzymes are common targets of toxicants. The enzyme
  effects may be specific, such as the inhibition of
  acetylcholinesterase (AChE). They may be reversible,
  such as the case with a number of carbamate
  insecticides on AChE. Irreversible enzyme inhibition
  is exemplified by DFP (diisopropyl fluorophosphate),
  which covalently bind with the enzymes. The effects
  may be nonspecific. For example, lead and mercury
  are inhibitors of a great variety of enzymes.
The last step of the oxidation of many chemicals
 is catalyzed by the cytochrome oxidase chain.
 Hydrocyanic acid (HCN) can bind with the
 iron in these enzymes and block their redox
 function. The aerobic respiration of cells is
 then arrested.

Carriers such as hemoglobin can be affected by a toxicant through preferential
   binding. For example, carbon monoxide can bind hemoglobin at the site
   where oxygen is normally bound. Because of its greater affinity for
   hemoglobin, it can inactivate hemoglobin and cause manifestations of
   oxygen deficiency in tissues.

Oxygen transport can also be impaired by an accumulation of methemoglobin,
  which is an oxidation product of hemoglobin with no oxygen binding
  ability. In normal individuals, the trace amount of methemoglobin is readily
  reduced to hemoglobin. Certain toxicants, such as nitrites and aromatic
  amines, can enhance the formation of methemoglobin. People with
  glucose-6-phosphate dehydrogenase deficiency have a lower capacity to
  regenerate hemoglobin from methemoglobin and are thus prone to have

Structural Proteins
Extracellular structural proteins such as collagen are
  unlikely to be affected by toxicants. However,
  toxicants such as ozone and asbestos may cause an
  increase in fibroblasts and deposition of collagen in
  the lung. Intracellular structural proteins, such as
  cytoskeleton (the network of protein filaments and
  microtubules in the cytoplasm that controls cell
  shape), may be damaged by toxicants such as arsenic,
  paraquat, benzene, styrene, and deoxynivalenol.
Coenzymes are essential for the normal function of
  enzymes. Their levels in the body can be diminished
  by toxicants that inhibit their synthesis. For example,
  pyrithiamine can inhibit thiamine kinase, which is
  responsible for the formation of the coenzyme
  thiamine pyrophosphate. NADPH can be destroyed in
  the presence of free radicals.

Metal-dependent enzymes can be inhibited by chelating
 agents (e.g., cyanides and dithiocarbamates) through
 removal of metal coenzymes such as copper and zinc.
Peroxidation of polyenoic fatty acids has been suggested as a mechanism of
   the necrotizing action of a number of toxicants, such as carbon
   tetrachloride, ozone, and estrogen.
The general anesthetics, ether and halothane, as well as many other lipophilic
   substances can accumulate in the cell membranes and thereby interfere with
   transport of oxygen and glucose into the cell. The cells of the central
   nervous system are especially susceptible to a lowering of oxygen and
   glucose level and are therefore among the first to be deleteriously affected
   by these substances.
Membrane dissolution can follow contact with organic solvents and
   amphoteric detergents. The ions of mercury and cadmium can complex
   with phospholipid bases and expand the surface area of the membrane,
   thereby altering its function. Lead ion can increase the fragility of
   erythrocytes and result in hemolysis. The oxygen-carrying function of
   hemoglobin is lost after it escapes from the hemolyzed erythrocytes.

Nucleic Acids
Covalent binding between a toxicant (such as alkylating
  agents) and replicating DNA and RNA can induce
  cancer, mutations, and teratogenesis. Such toxicants
  may also exert immunosuppressive effects.

Antimetabolites (are a major family of cytotoxic
  substances, that disturb or block one or more of the
  metabolic pathways ) such as methotrexate may be
  incorporated into DNA and RNA and then interfere
  with their replication.

Hypersensitivity reactions result from repeated exposure to a
  particular substance or to its chemically related substances.
  The latter phenomenon is referred to as cross sensitization.
  The substance, if it is a large polypeptide, acts as an antigen
  and stimulates the body to form antibodies. Otherwise, the
  substance acts as a hapten and combines with proteins in the
  body to form antigens. The reaction between an antigen from a
  later exposure and the corresponding antibodies results in the
  release of histamine, bradykinin, and others. The reaction has a
  typical pattern irrespective of the nature of the antigen.

Corrosive agents such as strong acids and bases can
  destroy local tissues by precipitating cellular proteins.
  Irritation of the underlying tissues occurs as a
Blockade of renal and biliary tubules may follow the
  precipitation of relatively insoluble toxicants or their
  metabolites. For example, acetylsulfapyridine, a
  metabolite of sulfapyridine, may block renal tubules,
  and harmol glucuronide from harmol may produce
       Body weight versus area
On a body weight basis, it is assumed for toxicity data
 extrapolation that humans are usually about 10 times
 more sensitive than rodents. On a body surface–area
 basis, humans usually show about the same
 sensitivity as test mammals, i.e. the same dose per
 unit of body surface area will give the same given
 defined effect, in about the same percentage of the
 population. Knowing the above relationships, it is
 possible to estimate the exposure to a chemical that
 humans should be able to tolerate.
        Chemical Interactions
1.  Additive effect
The simplest interaction is an additive effect: this
    is an effect, which is the result of two or
    more chemicals acting together and is the
    simple sum of their effects when acting
    independently. In mathematical terms,
1 + 1 = 2, 1 + 5 = 6, etc.
The effects of organochlorine pesticides are
    usually additive.
2. Synergistic (multiplicative) effect

This is an effect of two chemicals acting together,
  which is greater than the simple sum of their effects
  when acting alone; it is called synergism. In
  mathematical terms,
1 + 1 = 4, 1 + 5 = 10, etc.
Asbestos fibers and cigarette smoking act together to
  increase the risk of lung cancer by a factor of 40,
  taking it well beyond the risk associated with
  independent exposure to either of these agents.

3. Potentiation

In potentiation, a substance that on its own causes no
   harm makes the effects of another chemical much
   worse. This may be considered to be a form of
   synergism. In mathematical terms,
0 + 1 = 5, 0 + 5 = 20, etc.
For example, isopropanol, at concentrations that are not
   harmful to the liver, increases (potentiates) the liver
   damage caused by a given concentration of carbon

4. Antagonism

The opposite of synergism is antagonism: an
  antagonistic effect is the result of a chemical
  counteracting the adverse effect of another; in other
  words, the situation where exposure to two chemicals
  together has less effect than the simple sum of their
  independent effects. Such chemicals are said to show
  antagonism. In mathematical terms:
  1 + 1 = 0, 1 + 5 = 2, etc.
For example, histamine lowers arterial pressure, while
  adrenaline raises arterial pressure
Epidemiology and human toxicology
Epidemiology is the analysis of the distribution
 and determinants of health-related states or
 events in human populations and the
 application of this study to the control of
 health problems. It is the only ethical way to
 obtain data about the effects of chemicals on
 human beings and hence to establish beyond
 doubt that toxicity to humans exists. The
 following are the main approaches that have
 been used in epidemiology.
               1. Cohort study
A cohort is a component of the population born during a
  particular period and identified by the period of birth
  so that its characteristics (such as causes of death and
  numbers still living) can be ascertained as it enters
  successive time and age periods. The term „cohort‟
  has broadened to describe any designated group of
  persons followed or traced over a period of time.

Therefore, the group is selected before the study is

In a cohort study, one identifies cohorts of
  people who are, have been, or in the future
  may be exposed or not exposed, or exposed in
  different degrees, to a factor or factors
  hypothesized to influence the probability of
  occurrence of a given disease or other
  outcome. An essential feature of the method is
  the observation of a large population for a
  sufficient (long) period, to generate reliable
  incidence or mortality rates in the population

   Cohort study of lung cancer and
                              Lung cancer              No lung cancer
                                 cases                     control
Smokers                           127 (a)                    35 (b)

Nonsmokers                         73 (c)                    165 (d)

400 persons were selected and followed for a long period of time, with regards
to smoking and development of lung cancer.
These were classified according to being smoking or nonsmoking, developed
cancer or not
No. of smokers with lung cancer     a       127
No. of smokers without cancer       b        35
Total number of smokers            a+b      162
No of non smokers with cancer       c        73
No of non smokers without           d       165
Total No. of non smokers           c+d      238
Proportion of smokers who         a/(a+b)   0.784
develop cancer
Proportion of non smokers who     c/(c+d)   0.307
develop cancer
                 Relative risk
   Relative risk (RR) is defined as:
   RR = {(a/a+b)}/{c/(c+d)}
   RR = {127/(127+35)}/{73/(73+165)}
   RR = 2.6

   This means that the probability of developing
    cancer is 2.6 times as high in smokers as in
    nonsmokers. This is an evidence of association
    of cancer with smoking.
             2. Case-control study
A case-control study starts with the identification of persons with
  the disease (or other outcome variable) of interest, and a
  suitable control (comparison and reference) group of persons
  without the disease. The relationship of an attribute to the
  disease is examined by comparing the diseased and non-
  diseased with regard to how frequently the attribute is present
  or, if quantitative, the levels of the attribute, in the two groups.

Example: cancer suspected due to nuclear material exposure
To examine the effect of exposure of pregnant women to
  pesticides on the birth weight of the infants

  Case control study of lung cancer
            and smoking
                            Lung cancer              No lung cancer
                               cases                     control
Smokers                         127 (a)                    35 (b)

Nonsmokers                       73 (c)                   165 (d)

Total                             200                        200

 200 persons with lung cancer (cases) and 200 persons without lung cancer
  (control) were selected and categorized to whether they are smokers or
                               nonsmokers                                 33
             Odds (Chance) ratio
   OR is a measure of association between exposure and
   OR = (a/c)/(b/d)
   Odds of exposure among cases = a/c =127/73 = 1.7397
   Odds of exposure among control = b/d = 35/165 = 0.2121
   Odds ratio = 1.7397/0.2121 = 8.2
   This means that the odds of exposure to smoking among cases
    of lung cancer are 8.2 times as large as the odds of smoking
    among control. This indicates an important association
    between lung cancer and smoking.

                  Risk factors
   Risk factors are factors that increase the
    probability of having the disease
   Protective factors are factors that will decrease
    the probability of having the disease. This is
    implied by an odd ratio less than 1

   Case control study of obesity and
      regularly eating vegetable
                   Obese cases   No obese control

Eat vegetable        121 (a)         171 (b)

Do not eat veget     129 (c)          79 (d)

Total                 250              250

                      Odds ratio

   OR = (a/c)/(b/d)
   Odds of exposure among cases = a/c =121/129 = 0.938
   Odds of exposure among control = b/d = 171/79 = 2.1646
   Odds ratio = 0.938/2.1646 = 0.43
   This means that the odds of exposure to eating veget among
    obese persons were 0.43 times as large as the odds among non
    obese control. This indicates that eating vegets could be a
    protective factor decreasing the probability of obesity

Case-control study of depression and
     regularly eating vegetable
                   Depressed cases   undepressed
Eat vegetable          90 (a)          90 (b)

Do not eat veget       130 (c)         130 (d)

Total                   220             220

                       Odds ratio
   OR = (a/c)/(b/d)
   Odds of exposure among cases = a/c = 90/130 = 0.6923
   Odds of exposure among control = b/d = 90/130 = 0.6923
   Odds ratio = 0.6923/6923 = 1.0
   This means that the odds of exposure to eating veget among
    depressed persons were the same as the odds among
    undepressed control. This indicates that eating vegets has no
    association with depression.
   Conclusion: OR>1 suggests a possible risk factor, an OR<1
    suggests a possible protective factor, while an OR=1 suggests
    no association between exposure and outcome

    Advantages of a case-control study
   Can be easily used for studying infrequent
   Relatively inexpensive as no follow-up is
   Fast process, as no need to wait for the
    accumlation of enough cases as in cohort
   Cheaper to do than cohort studies
3. Confounding studies

A confounding variable is a variable (say,
 pollution) that can cause the disease under
 study (cancer) and is also associated with the
 exposure of interest (smoking). The existence
 of confounding variables in smoking studies
 made it difficult to establish a clear causal link
 between smoking and cancer unless
 appropriate methods were used to adjust for
 the effect of the confounders

    An example of a confounding study
   We are studying categories which developed a disease and
    did not develop a disease as a consequence of exposure.
   First a table like the one below is constructed:

                         diseased    No disease       Total

Exposed                      a             b           a+b

Not exposed                  c             d           c+d

No. of people with disease                a
No. of people without disease             b
Total number of exposed                  a+b
No of unexposed but diseased              c
No of unexposed without disease           d
Total No. of unexposed                   c+d
Proportion of exposed who developed     a/(a+b)
Proportion of unexposed who developed   c/(c+d)
    Three cases:
1.   a/(a+b) > c/(c+d)
This means that exposure and disease are positively
2. a/(a+b) < c/(c+d)
This means that exposure and disease are negatively
     associated. Exposure is thus a protective factor
3. a/(a+b) = c/(c+d)
This means that there is no association between
     exposure and disease

   The relative risk (RR) is a measure of the
    degree of association between the exposure
    and development of the disease
   RR = {(a/a+b)}/{c/(c+d)}
   RR>1 means that exposed individuals have
    higher probability of developing the disease
   RR<1 means that exposure leads to less risk of
    the disease, i.e. exposure is a protective factor
   RR=1 suggests no association between
    exposure and disease

      Bedsore Mortality Example
   9400 cases where identified in a hospital as having or
    not having bedsores. These were followed to
    determine whether with regards to death and whether
    there is an association between bedsores and death.
   Among these, 824 have bedsores while 8576 did not
    have bedsore.
   Among these, 116 were admitted to hospital as high
    severity cases, while 9284 were low severity cases.

                    Detailed info:
   Of the 79 people who had bedsores and died 55 had high
    medical severity and 24 had low medical severity
   Of the 745 people who had bedsores and did not die, 51 had
    high medical severity and 694 had low medical severity
   Of the 286 people who had no bedsores and died, 5 had high
    medical severity and 281 had low medical severity
   Of the 8290 people who had no bedsores and did not die, 5 had
    high medical severity and 8285 had low medical severity.
   Now construct a table for each category:

              Association of interest

Bedsores                                     Death

association                             association

                  Medical severity

        First: Is medical severity a
           confounding factor?
   To answer this question we should check
    whether medical severity is associated with
    exposure (bedsores) and endpoint (death).
   This can be simply done by constructing the
    appropriate association tables.

Association between medical
   severity and bedsores
                          High     Low     Total
                         Medical Medical
                         Severity Severity
Bedsore                    106      718    824

No bedsore                  10        8566       8576

RR = (106/824)/(10/8576) = 110
Therefore, a strong association is present between medical
severity status and bedsores
Association between medical severity
             and death
                                die       Did not       Total
High Medical Severity           60          56           116

Low Medical Severity           305          8979        9284

RR = (60/116)/(305/9284) = 15.7
Therefore, a strong association is present between medical
severity status and death

   It is clear that medical severity has strong
    association with bedsores and death.

                        Association of interest

            Bedsores                                  Death

            RR=110                                RR=15.7

                            Medical severity
  Now let us look at: Bedsores and
                  died   Did not   Total
Bedsore           79      745      824

No bedsore        286     8290     8576

Total             365     9035     9400

No. of people with bedsore who died         a        79
No. of smokers with bedsore who did         b       745
not die
Total number of people with bedsore       a+b       824
No of people without a bedsore who          c       286
No of people without bedsore who did        d       8290
not die
Total No. of people without a bedsore     c+d       8576
Proportion of people with a bedsore      a/(a+b)    9.6%
who died
Proportion of people without a bedsore   (c/(c+d)   3.3%
who died                                                   56
   RR = (79/824)/(286/8576) = 2.9
   This suggests that the probability of death was 2.9
    times as high in people with bedsores as in people
    without bedsores. This may imply a strong
    association between bedsores and death.

   Do not rush with conclusions as we have to check the
    effect of the confounding factor; where when patients
    where admitted to hospital the medical severity of
    their cases was recorded, leading to two categories:
    high severity and low severity groups.

   Our task now is to check whether medical severity is
    a confounding factor or not. Two tables will be
    necessary, one for each category.
   The point is to study association between exposure
    (bedsores) and endpoint (death) for each case of
    medical severity. i.e. one for the high medical
    severity cases and the other for low medical severity
    cases. This means that we eliminate the effect of the
    suspected confounding factor by keeping it constant
    in both cases.
    High medical severity group
                 died   Did not   Total
Bedsore          55       51      106

No bedsore        5       5        10

Total            60       56      116

   The relative risk in the high medical severity

   RR = (55/106)/(5/10) = 1.04

    Low medical severity group
                died   Did not   Total
Bedsore          24     694      718

No bedsore       281    8285     8566

Total            305    8979     9284

   The relative risk in the low medical severity

   RR = (24/718)/(281/8566) = 1.02

   This process is called stratification where two strata
    where created. In each stratum, the association
    between bedsores and death cannot be explained by
    medical severity because in each stratum medical
    severity was kept constant.
   Stratification is used to adjust for a confounding
   Both relative risks of both strata are close to 1. This
    means that the risk of death has no association to
    bedsores, provided that adjustment is made for
    medical severity.

              No Association

Bedsores                              Death

association                      association

              Medical severity

    The unadjusted relative risk of 2.9 is thus
     misleading. Therefore, trying to eliminate bedsores
     will not affect probability of death. If A does not
     cause B, then eliminating A will not affect the
     occurrence of B. Bedsores are said to be guilty by
    If the adjusted and unadjusted relative risk have
     been similar, this means that medical severity did
     not confound the association between bedsores and
    For confounding to occur:
1.   There must be an association between cofounder
     and the disease
2.   There must be an association between cofounder
     and exposure

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