By J. Vatras. Conway School of Landscape Design.
With the ligand-based approaches order propecia with amex, prior ligand knowledge is required propecia 1mg generic, from which new ligands are derived cheap 5 mg propecia overnight delivery. Examples of the first class are xanthine derivatives such as caffeine (compound 2 in Figure 1) and theophylline. These compounds share some structural resemblance: by aligning them using the furan substituent as an anchor, two rings of their ring system and the amine groups overlap. The (often unsubstituted) furan moiety is a common structural feature found in many A2A 23,24 antagonists. It is typically linked to a triazole ring that is part of a ring-fused system of two or three heterocycles, with a single amine group attached. Describing molecules in terms of molecular parts, or fragments, such as rings and ring systems, substituents and functional groups, provides an abstraction for chemists to reason about molecules. This helps, for instance, for identification of a common core in a set of molecules, or for finding the substituents that contribute to bioactivity. Although these approaches may be relatively assured of success, truly new chemistry is only seldom being discovered this way; structural derivatives often resemble their parent structures. The similarity between known ligands that are analysed and the proposed candidates that result may be reduced by analyzing the structures in closer detail; instead of only focusing on predefined fragments, one might consider all possible structural patterns that are present in the molecules. This may result in the discovery of more unusual structural patterns that potentially offer greater diversity when applied to the discovery of new ligands. This method was designed to be complementary to the recent structure-based virtual screening studies, although it was exclusively based on ligand information only. This so-called frequent substructure 150 Substructure-based Virtual Screening mining, i. Several screening models were constructed by varying the parameters of substructure generation and score calculation. These models were benchmarked and the best performing model was subsequently applied for large-scale screening on a commercial vendor library. The first set consisted of 892 low-affinity antagonists, with activity values between 5. For analysis of the antagonists, each antagonist set was compared against a background set of 10,000 drug-like molecules. Each pair of antagonist and background sets represented an individual training set from which a model was created. To analyze the structural features of the molecules, the molecular structures were first converted into a machine readable format, i. In addition to normal chemical representation, translation into one of three elaborate chemical representations was also explored as alternative representations when converting the source molecules to graphs. The translated source and background compounds were then subjected to frequent substructure mining. Frequent substructure mining is a data mining technique that finds all frequently occurring substructures that are present in a preset 151 Chapter 5 26,27 minimum number of molecules, which in this study was set to 30% of the size of the set; a substructure is defined as any part of the molecule, ranging from a single atom to the complete structure. The number of generated substructures for each source set and chemical representation are provided in Table 1. In general, smaller sets, such as the high-affinity antagonists set (255 molecules) result in significantly higher numbers of generated substructures compared to larger sets, such as the low-affinity (892 molecules) and combined antagonist (1,147 molecules) sets. With increasing set size, the chance for an individual substructure to occur more frequently than the set minimum decreases, resulting in finding fewer substructures. In addition, the high mutual similarity between antagonists in the high-affinity set results in more substructures with frequencies above the support threshold. Number of generated substructures for each source set and chemical representation. Activity Range [a] Representation pKi ≥ 8 pKi ≥ 5 5 ≤ pKi < 8 Normal 4,424 471 408 Ar. Examples of discriminative substructures for high-affinity adenosine A2A antagonists versus drug-like background compounds. Note that the provided examples are all within the set of the 50 top ranking substructures. A2A Background Score Nr Substructure antagonists compounds contribution a N N 242 (94. Note that the provided examples are all within the set of the 50 top ranking substructures (described below). All substructures in Table 2 are also present in compound 1 (note that substructures may overlap). For two of these substructures, c and d of Table 2, this is illustrated in Figure 2. This figure shows one example of how substructures are positioned in the molecules they originate from. Note that the methanediamine substructure, c, occurs three times in compound 1 (and also once in compound 3 and twice in compound 2, Figure 1). For frequency calculations, however, it was counted 153 Chapter 5 only once per molecule. Substructures c and d each represent one of two types of substructures that exist: substructures that are clear molecular fragments such as rings and functional groups (d) and substructures that have an unspecified shape (c). The structure of compound 1 with two examples of discriminative substructures for A2A antagonists highlighted in bold. For each frequent substructure in the antagonist set, the occurrence in the background set was also determined. For instance, substructure c in Table 2 occurred in 247 of the 255 A2A antagonists, or 96. Since these substructures are frequently occurring in the A2A antagonists and infrequently in background compounds they are signified as ‘discriminative’ for A2A antagonists. This discriminative property is quantified by calculating the difference between the fraction of antagonists and the fraction of background compounds in which the substructures occur. This difference is referred to as ‘score contribution’ of a substructure and is used to rank the substructures. The top-ranked substructures, those with the highest score contribution, are the most ‘discriminative’ ones and were subsequently used for the screening. This is because the frequency of a in the background set is considerably lower than that of b, resulting in a higher score contribution for a. Compounds with the highest score were considered more likely to be A2A antagonists. The first two sets consisted of the top 50 and the top 100 of the ranked substructures. The third set consisted of substructures ranked at positions 51 to 100 and served to investigate the impact of substructure ranking on screening. Three different methods of score calculation were tested: first, simple counting of substructures that match the chemical structure of a screening compound; second, summation of the score contributions of the matching substructures; third, multiplication of (the score contribution + 1) of all matching substructures.
In fact buy generic propecia 1 mg on line, phenytoin does not have a true half-life; its half-life is dependent on drug concentration best propecia 1mg. The major factor in determining how long it will take to attain steady state is the difference between Vmax and the daily dose purchase propecia 1 mg otc. This relationship between Vmax and concentration can be derived mathematically by examining the equations used to calculate dose for first- and zero-order models. We will start by rearranging two definitions in the first-order model: (See Equation 1-1. Examination of this equation shows that when Css is very small compared to Km, Clt will approximate Vmax/Km, a relatively constant value. Therefore, at low concentrations, the metabolism of phenytoin follows a first-order process. However, as Css increases to exceed Km, as is usually seen with therapeutic concentrations of phenytoin, Cl will decrease and metabolism will convert tot zero order. We can calculate an estimate of the time it takes to get to 90% of steady state using the following equation: (See Equation 10-4. This equation is derived from a complex integration of the differential equation describing the difference between the rate of drug coming in (i. This equation gives us an estimate of when to draw steady-state plasma concentrations and is based on the assumption that the beginning phenytoin concentration is zero. Clinical Correlate The t90% equation is a very rough estimate of time to 90% of steady state and should be used only as a general guide. The clinician should check nonsteady-state phenytoin concentrations before this time to avoid serious subtherapeutic or supratherapeutic concentrations. Therefore: Note how the units cancel out in this equation, leaving the answer expressed in days, not hours. Close inspection of this calculation illustrates the impact that the denominatorthe difference of Vmax and daily dosehas on the time it takes to reach steady state. A phenytoin plasma concentration of 6 mg/L is drawn 18 days after the beginning of therapy. Calculate an appropriate dosing regimen to attain our desired concentration of 15 mg/L. Phenytoin pharmacokinetic dosing calculations are not as accurate and predictive as those for the aminoglycosides and theophylline; therefore, good clinical judgment is required when recommending a dose. Remember, time to 90% of steady state (t90%) is dependent on plasma drug concentration. Therefore: Note that our new t90% is slightly smaller than the previous estimate because the difference between Vmax and dose is now greater. He is now seizure free, and his physician would like to adjust his dose to get his plasma concentration back to 15 mg/L. What dose would you now recommend to achieve a plasma phenytoin concentration of 15 mg/L? Now that we have measured two different steady-state concentrations at two different doses, we can make an even more accurate dosing change. As shown in Lesson 14, clearance can be expressed as Xd/Css, resulting in the plot in Figure 15-5. We can now plot both steady-state doses (X1 of 418 mg/day and X2 of 552 mg/day) on the y-axis and both steady-state Xd/Css values (X1/C1 = 418/6 L/day and X2/C2 = 552/20 L/day) on the x-axis, thus linearizing these relationships. The slope of the line, which represents -Km, can now be calculated as follows: (See Equation 10-2. Clinical Correlate It is important to dose phenytoin correctly so side effects do not occur. Dose-related side effects at serum concentrations greater than 20 mcg/mL include nystagmus, whereas concentrations greater than 30 mcg/mL and more may result in nystagmus and ataxia. Concentrations greater than 40 mcg/mL may produce ataxia, lethargy, and diminished cognitive function. Adverse effects that may occur at therapeutic concentrations include gingival hyperplasia, folate deficiency, peripheral neuropathy, hypertrichosis, and thickening of facial features. Relationship of daily dose to the dose divided by steady-state concentration achieved. Note that the units of plasma concentrations for digoxin are different (nanograms per milliliter) from those of other commonly monitored drugs (usually milligrams per liter). The steady-state volume of distribution (Vss) of digoxin is large and extremely variable. The Vss ranges from: 15-2 in subjects with normal renal function, whereas in patients with renal failure the average Vss is 4. Digoxin is approximately 25% bound to serum protein in patients with normal creatinine 1 2 clearance. This reduction in protein binding of digoxin is thought to be due to displacement of digoxin by endogenous substances that are not cleared efficiently in patients with renal dysfunction. When calculating digoxin dosage, the bioavailability (F) of oral dosage forms is an important consideration. For patients with normal oral absorption, digoxin tablets are 50-90% (average, 75%) absorbed (F = 0. Systemic clearance (Cl ) of digoxin is determined by both renal (Clt r) and nonrenal, or metabolic, 2 clearance (Clm). She is 65 inches tall and weighs 57 kg, her blood pressure is 130 mm Hg/84 mm Hg, and her serum creatinine concentration is 1. Her physician would like to begin a daily oral maintenance dose to achieve a steady-state plasma concentration of 1. Recommend an oral maintenance dose of digoxin in this patient to achieve a steady-state plasma concentration of 1. The relationship between the steady-state plasma concentration, maintenance dose, and total systemic clearance is shown below: 15-5 (See Equation 4-3. The physician holds digoxin therapy for 2 days and asks you to recommend a new dose to achieve a target concentration of 1. In this problem, the patient may have deteriorating renal function, or, she may have consumed more digoxin than was prescribed. He has chronic renal insufficiency and is diagnosed as having heart failure, for which his physician recommends beginning digoxin. Calculate a maintenance dose of digoxin to achieve a steady-state digoxin concentration of 0. Recall that the total systemic clearance and renal clearance of digoxin must be calculated: Clr(mL/minute) = 0. Now we can calculate a daily digoxin maintenance dose for this patient: (See Equation 15-5. This method of administration prevents the propylene glycol contained in this formulation from causing cardiovascular collapse. Calculation of T1/2 from K, or K from T1/2 for First-Order, One-Compartment Model (See p. Calculation of Km, the "Michaelis Constant" (mg/L), Representing the Drug Concentration at Which the Rate of Elimination is Half the Maximum Rate (Vmax) for Zero-Order (i. Note: should be rounded off to a practical dosing interval such as Q 8 hours, Q 12 hours, etc.
The importance of these differences in perfusion is that for most drugs the rate of delivery from the circulation to a particular tissue depends greatly on the blood flow to that tissue effective propecia 5 mg. Perfusion rate limitations occur when the membranes present no barrier to distribution generic 5 mg propecia visa. If the blood flow rate increases order propecia now, the distribution of the drug to the tissue increases. Therefore, drugs apparently distribute more rapidly to areas with higher blood flow. Highly perfused organs rapidly attain drug concentrations approaching those in the plasma; less well-perfused tissues take more time to attain such concentrations. Furthermore, certain anatomic barriers inhibit distribution, a concept referred to as permeability-limited distribution. This situation occurs for polar drugs diffusing across tightly knit lipoidal membranes. It is also influenced by the oil/water partition coefficient and degree of ionization of a drug. For example, the blood-brain barrier limits the amount of drug entering the central nervous system from the bloodstream. This limitation is especially great for highly ionized drugs and for those with large molecular weights. After a drug begins to distribute to tissue, the concentration in tissue increases until it reaches an equilibrium at which the amounts of drug entering and leaving the tissue are the same. The drug concentration in a tissue at equilibrium depends on the plasma drug concentration and the rate at which drug distributes into that tissue. In highly perfused organs, such as the liver, the distribution rate is relatively high; for most agents, the drug in that tissue rapidly equilibrates with the drug in plasma. In several disease states, such as liver, heart, and renal failure, the cardiac output and/or perfusion of blood to various tissues are altered. A decrease in perfusion to the tissues results in a lower rate of distribution and, therefore, a lower drug concentration in the affected tissues relative to the plasma drug concentration. When the tissue that receives poor perfusion is the primary eliminating organ, a lower rate of drug elimination results, which then may cause drug accumulation in the body. A drug that is highly lipid soluble easily penetrates most membrane barriers, which are mainly lipid based, and distributes extensively to fat tissues. This difference becomes important when determining loading dosage requirements of drugs in overweight patients. If total body weight is used to estimate dosage requirements and the drug does not distribute to adipose tissue, the dose can be overestimated. Clinical Correlate In general, volume of distribution is based on ideal body weight for drugs that do not distribute well into adipose tissue and on total body weight for drugs that do. Once in breast tissue, the alkaline drugs ionize because breast tissue has an acidic pH; therefore, the drugs become trapped in this tissue. Due to the nature of biologic membranes, drugs that are un-ionized (uncharged) and have lipophilic (fat-soluble) properties are more likely to cross most membrane barriers. Many of these factors can be incorporated into a relatively simple physiologic model. The equation below represents this physiologic model and provides a conceptual perspective of the volume of distribution: V = Vp + Vt(Fp/Ft) where: V = volume of distribution, Vp = plasma volume, Vt = tissue volume, Fp = fraction of unbound drug in the plasma, and Ft = fraction of unbound drug in the tissue. From this model, it is evident that the volume of distribution is dependent on the volume of the plasma (3-5 L), the volume of the tissue, the fraction of unbound drug in the plasma, and the fraction of unbound drug in the tissue. In contrast to plasma protein binding, tissue protein binding cannot be measured directly. We use this equation to help us understand why the volume of distribution of a drug may have changed as a consequence of drug interactions or disease states. Usually, changes in the volume of distribution of a drug can be attributed to alterations in the plasma or tissue protein binding of the drug. The clinical consequence of changes in the volume of distribution of a drug in an individual patient is obvious. Because the initial plasma concentration of the drug (C0) is primarily dependent on the size of the loading dose and the volume of distribution (C0 = loading dose/V), changes in any of these parameters could significantly alter the C0 achieved after the administration of a loading dose. Therefore, one must carefully consider the loading dose of a drug for a patient whose volume of distribution is believed to be unusual. Phenytoin can be used to describe changes in the factors determining volume of distribution. For a typical 70-kg person, the volume of distribution for phenytoin is approximately 45 L. If we assume that the plasma volume is 5 L, the tissue volume is 80 L, and the fraction unbound in tissue is 0. Protein binding in plasma can range from 0 to >99% of the total drug in the plasma and varies with different drugs. The extent of protein binding may depend on the presence of other protein-bound drugs and the concentrations of drug and proteins in the plasma. The usual percentages of binding to plasma proteins for some commonly used agents are shown in Table 8-1. Theoretically, drugs bound to plasma proteins are usually not pharmacologically active. Although only unbound drug distributes freely, drug binding is rapidly reversible (with few exceptions), so some portion is always available as free drug for distribution. The association and dissociation process between the bound and unbound states is very rapid and, we assume, continuous (Figure 8-3). The binding of a drug to plasma proteins will primarily be a function of the affinity of the protein for the drug. The percentage of protein binding of a drug in plasma can be determined experimentally as follows: where [total] is the total plasma drug concentration (unbound drug + bound drug) and [unbound] refers to the unbound or free plasma drug concentration. Another way of thinking about the relationship between free and total drug concentration in the plasma is to consider the fraction of unbound drug in the plasma (Fp). Fp is determined by the following relationship: Although the protein binding of a drug will be determined by the affinity of the protein for the drug, it will also be affected by the concentration of the binding protein. Two frequently used methods for determining the percentage of protein binding of a drug are equilibrium dialysis and ultrafiltration. Three plasma proteins are primarily responsible for the protein binding of most drugs. Although only the unbound portion of drug exerts its pharmacologic effect, most drug assays measure total drug concentrationboth bound and unbound drug. Therefore, changes in the binding characteristics of a drug could affect pharmacologic response to the drug.
The informant should know that a drug of itself cannot force him to tell the truth buy on line propecia, although it may make him talkative cheap propecia 5 mg on-line, overemotional buy 5 mg propecia with visa, mentally confused, or sleepy. He should also know that the effects of drugs are quite variable from individual to individual, and that those who may use drugs against him cannot predict with certainty what effects will occur in his particular case. To a victim of such attempts the imperfect predictability of many of the direct effects and side effects of any drug offers many opportunities for simulation. It is likely that most nonfatal drugs will have a transient, time-limited action rather than a permanent one. There is no need for the informant to become panicky at any bizarre or uncomfortable reactions he may experience, for these reactions will probably disappear. Finally, since the interrogator wants accurate and factual information, the informant can confound the interrogator as to what is fact and fiction by a number of means. He can simulate drowsiness, confusion, and disorientation early during the administration of the drug. As a final suggestion, this reviewer is inclined to agree with West (130) that the basic training of military personnel can be helpful in developing techniques of resistance to interrogation. A brief course on the limitations of the use of drugs in interrogation and on the kinds of pharmacologic effects to be expected from the different types of drugs would be helpful. Such training could decrease the fear, hypersuggestibility, and other deleterious reactions that evolve from the uncertain, the unpredictable, and the unknown. A comparative behavior and psychodynamic study of reserpine and equally potent doses of raudixin in schizophrenics. Zur Pharmakologie des Reserpin, eines neuen Alkaloids aus Rauwolfia serpentina Benth. Drugs that produce deviations in mood, including anxiety presumably without impairing capacities for orientation or at least secondarily to changes in mood. Sodium amytal as an aid in state hospital practice: Single interviews with 100 patients. Pharmacologic explorations of the personality: Narcoanalysis and "methedrine" shock. Clinical studies on alpha (2-piperidyl) benzhydrol hydrochloride, a new antidepressant drug. Experimental investigation into the validity of confessions obtained under sodium amytal narcosis. The relationship of gender and intelligence to choice of words: A normative study of verbal behavior. The speech patterns of schizophrenic patients: A method of assessing relative degree of personal disorganization and social alienation. Motivational determinants in the modification of behavior by morphine and pentobarbital. In Group for the Advancement of Psychiatry, Methods of forceful indoctrination: Observations and interviews. A preliminary investigation into abreaction comparing methedrine and sodium amytal with other methods. Relationship between effects of a number of centrally acting drugs and personality. A controlled investigation into the value of chlorpromazine in the management of anxiety states. The effects of methedrine and of lysergic acid diethylamide on mental processes and on the blood adrenaline level. Mental effects of reduction of ordinary levels of physical stimuli on intact healthy persons. Psychological changes in normal and abnormal individuals under the influence of sodium amytal. Modifications in ego structure and personality reactions under the influence of the effects of drugs. Intravenous barbiturates: An aid in the diagnoses and treatment of conversion hysteria and malingering. Studies in psychopharmacologic psychotherapy: Effective psychotherapy during drug- induced states. Electroencephalogram and psychopathological manifestations in schizophrenia as influenced by drugs. The transference and non-specific drug effects in the use of the tranquilizer drugs, and their influence on affect. Clinical study of the mescaline psychosis with special reference to the mechanism of the genesis of schizophrenia and other psychotic states. The serial administration of the "amytal test" for brain disease; its diagnostic and prognostic value. Freed in Symposium: Discussion and critique on methodology of research in psychiatry. Effects of suggestion and conditioning on the action of chemical agents in human objects — the pharmacology of placebos. This chapter reviews the responses available for such interpretations, the validity of the method, and possible improvements and extensions of its use which may occur in the future. Such practices, long based on supernatural principles, have in fact been used since ancient times (35). As long ago as the eighteenth century Daniel Defoe proposed a test of this sort with a scientific rationale (31). Actual experiment on physiologic tests of deception seems to have begun with psychologists in Germany early in the century, with Benussi (2), an Italian with German training, offering the most extensive and promising results. A few years later Marston (29) and Larson (23), on the basis of certain experimental work, reported success with systolic blood pressure changes. Under a contract with the Office of Naval Research, which was -142- also supported by the other services, a group at Indiana University undertook a comparison of variables and combinations of variables that was reported in 1952 (17). Meanwhile the use of the "standard" methods has spread widely as an applied art with a certain body of tradition. An excellent survey of the current status of the field has recently been provided by Ferracuti (18) in Italian. The present survey will be organized into the following topics: (a) Evaluation of present practices. Evaluation of Present Practices We may consider "current practices" en masse and ask how effective these have been. This problem is considered by Inbau (20), apparently by comparing "lie detector" results with jury verdicts and confessions. The agreement between detection and the criterion for various sets of data is about 70 per cent, with 20 per cent of the cases discarded as ambiguous. This figure must be compared, of course, with some percentage of success to be expected by chance. If every case were treated independently, the percentage of success would be 50 per cent. It is, however, common practice to examine a group of suspected persons, of whom it is known that only one is guilty. If the operator then selects one from the group as guilty, his chances of being correct by sheer luck are less than 50 per cent.
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