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levels of exposure. Dose response relationships modelled by dose response curves are used extensively in pharmacology and drug development. In particular, the shape of a drug's dose–response curve (quantified by EC50, nH and ymax parameters) reflects the biological activity and strength of the drug.
1692:
Neubig, Richard R.; Spedding, Michael; Kenakin, Terry; Christopoulos, Arthur; International Union of
Pharmacology Committee on Receptor Nomenclature and Drug, Classification. (December 2003). "International Union of Pharmacology Committee on Receptor Nomenclature and Drug Classification. XXXVIII.
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The first point along the graph where a response above zero (or above the control response) is reached is usually referred to as a threshold dose. For most beneficial or recreational drugs, the desired effects are found at doses slightly greater than the threshold dose. At higher doses, undesired
259:
for mechanical pressure. However, stimuli (such as temperatures or radiation) may also affect physiological processes beyond sensation (and even give the measurable response of death). Responses can be recorded as continuous data (e.g. force of muscle contraction) or discrete data (e.g. number of
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appear and grow stronger as the dose increases. The more potent a particular substance is, the steeper this curve will be. In quantitative situations, the Y-axis often is designated by percentages, which refer to the percentage of exposed individuals registering a standard response (which may be
242:
reflects how a small amount of a toxin has no significant effect, while a large amount may be fatal. This reflects how dose–response relationships can be used in individuals. In populations, dose–response relationships can describe the way groups of people or organisms are affected at different
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Dose–response relationships generally depend on the exposure time and exposure route (e.g., inhalation, dietary intake); quantifying the response after a different exposure time or for a different route leads to a different relationship and possibly different conclusions on the effects of the
499:
of the hypothetical response to agonist, log concentration on the x-axis, in combination with different antagonist concentrations. The parameters of the curves, and how the antagonist changes them, gives useful information about the agonist's pharmacological profile. This curve is similar but
493:
1272:
992:, or grams per kilogram of body-weight for oral exposures or milligrams per cubic meter of ambient air for inhalation exposures. Other dose units include moles per body-weight, moles per animal, and for dermal exposure, moles per square centimeter.
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219:
Studying dose response, and developing dose–response models, is central to determining "safe", "hazardous" and (where relevant) beneficial levels and dosages for drugs, pollutants, foods, and other substances to which humans or other
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may be more appropriate, depending on the circumstances. A recent critique of these models as they apply to endocrine disruptors argues for a substantial revision of testing and toxicological models at low doses because of observed
1837:
Vandenberg, Laura N.; Colborn, Theo; Hayes, Tyrone B.; Heindel, Jerrold J.; Jacobs, David R.; Lee, Duk-Hee; Shioda, Toshi; Soto, Ana M.; vom Saal, Frederick S.; Welshons, Wade V.; Zoeller, R. Thomas; Myers, John
Peterson (2012).
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stressor under consideration. This limitation is caused by the complexity of biological systems and the often unknown biological processes operating between the external exposure and the adverse cellular or tissue response.
769:
592:, or other methods such as the Spearman–Kärber method. Empirical models based on nonlinear regression are usually preferred over the use of some transformation of the data that linearizes the dose-response relationship.
978:). Such a curve is referred to as a quantal dose–response curve, distinguishing it from a graded dose–response curve, where response is continuous (either measured, or by judgment).
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Di Veroli, Giovanni Y.; Fornari, Chiara; Goldlust, Ian; Mills, Graham; Koh, Siang Boon; Bramhall, Jo L.; Richards, Frances M.; Jodrell, Duncan I. (1 October 2015).
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Roeland van Wijk et al., Non-monotonic dynamics and crosstalk in signaling pathways and their implications for pharmacology. Scientific
Reports 5:11376 (2015)
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577:, with the steepest portion in the middle. Biologically based models using dose are preferred over the use of log(dose) because the latter can visually imply a
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model is a generalization of the Hill equation where an effect can be set for zero dose. Using the same notation as above, we can express the model as:
927:{\displaystyle {\frac {E}{E_{\mathrm {max} }}}={\frac {^{n}}{{\text{EC}}_{50}^{n}+^{n}}}={\frac {1}{1+\left({\frac {\mathrm {EC} _{50}}{}}\right)^{n}}}}
149:. Low doses are insufficient to generate a response, while high doses generate a maximal response. The steepest point of the curve corresponds with an
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Hamilton, MA; Russo, RC; Thurston, RV (1977). "Trimmed
Spearman–Karber method for estimating median lethal concentrations in toxicity bioassays".
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The shape of dose-response curve typically depends on the topology of the targeted reaction network. While the shape of the curve is often
569:) can be studied. The applied dose is generally plotted on the X axis and the response is plotted on the Y axis. In some cases, it is the
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Some example measures for dose–response relationships are shown in the tables below. Each sensory stimulus corresponds with a particular
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The concept of linear dose–response relationship, thresholds, and all-or-nothing responses may not apply to non-linear situations. A
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The Hill equation can be used to describe dose–response relationships, for example ion channel-open-probability vs.
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1934:
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has developed extensive guidance and reports on dose–response modeling and assessment, as well as software. The
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1267:{\displaystyle E_{\mathrm {hill} }={\frac {{}^{n}\times {E_{\mathrm {max} }}}{{}^{n}+\mathrm {EC} _{50}^{n}}}}
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Specific to response to doses of radiation the Health
Physics Society (in the United States) has published a
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relating the magnitude of a dose (stimulus) to the response of a biological system. A number of effects (or
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on the origins of the linear no-threshold (LNT) model though the society has not adopted a policy on LNT."
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941:(such as EC50, IC50, ED50, etc.) and measures of efficacy (such as tissue, cell or population response).
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1983:
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model is the single most common model for describing dose-response relationship in drug development.
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1130:{\displaystyle E=E_{0}+{\frac {{}^{n}\times {E_{\mathrm {max} }}}{{}^{n}+\mathrm {EC} _{50}^{n}}}}
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distinct from that, which is generated with the ligand-bound receptor concentration on the y-axis.
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1635:"An automated fitting procedure and software for dose-response curves with multiphasic features"
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Statistical analysis of dose–response curves may be performed by regression methods such as the
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1840:"Hormones and Endocrine-Disrupting Chemicals: Low-Dose Effects and Nonmonotonic Dose Responses"
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is defined more broadly as the response from any type of stimulus, not limited to chemicals.
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1432:"Fundamental Carcinogenic Processes and Their Implications for Low Dose Risk Assessment"
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Please expand the section to include this information. Further details may exist on the
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236:. Dose response relationships may be used in individuals or in populations. The adage
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Severity of lesion, blood pressure, heart rate, extent of movement, attentiveness,
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A dose response curve showing the normalised tissue response to stimulation by an
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ATP production, proliferation, muscle contraction, bile production, cell death
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Macdougall, James (2006). "Analysis of Dose–Response
Studies—Emax Model".
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Typical experimental design for measuring dose-response relationships are
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are exposed. These conclusions are often the basis for public policy. The
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Crump, K. S.; Hoel, D. G.; Langley, C. H.; Peto, R. (1 September 1976).
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648:. A generalized model for multiphasic cases has also been suggested.
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Benchmark Dose
Software (BMDS) Version 2.1 User's Manual Version 2.0
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is the drug concentration (or equivalently, stimulus intensity) and
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is the drug concentration that produces a 50% maximal response and
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of the dose that is plotted on the X axis. The curve is typically
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also has guidance to elucidate dose–response relationships during
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1293:, in some cases non-monotonic dose response curves can be seen.
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about All the other models in drug development like "Emax"; try
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The parameters of the dose response curve reflect measures of
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with respect to the logarithm of the dose and is similar to a
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134:"Dose-response" redirects here. For the academic journal, see
29:
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curve, the half maximal effective concentration, where the EC
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1693:
Update on Terms and
Symbols in Quantitative Pharmacology".
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CDD Vault, Example of Dose-Response Curve fitting software
203:. This is explained further in the following sections. A
1792:. Statistics for Biology and Health. pp. 127–145.
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point is defined as the inflection point of the curve.
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1911:, with emphasis on toxicokinetic-toxicodynamic models
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ATP production, calcium signals, morphology, mitosis
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may also provide insights into the effect of drugs.
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Motivation for studying dose–response relationships
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1606:Nonlinear Regression Analysis and its Applications
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1738:"Single Channel Properties of P2X2 Purinoceptors"
958:Dose response curves are typically fitted to the
1748:(5). The Rockefeller University Press: 695–720.
632:Logarithmic dose–response curves are generally
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1475:United States Environmental Protection Agency
488:Analysis and creation of dose–response curves
8:
1604:Bates, Douglas M.; Watts, Donald G. (1988).
636:and monotonic and can be fit to a classical
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27:Measure of organism response to stimulus
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1570:Environmental Science & Technology
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1140:Compare with a rearrangement of Hill:
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1907:A website on mathematical models in
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505:Construction of dose–response curves
226:U.S. Environmental Protection Agency
58:adding citations to reliable sources
2728:Minimum bactericidal concentration
1473:(Draft ed.). Washington, DC:
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731:{\displaystyle \mathrm {EC} _{50}}
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675:is the magnitude of the response,
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1523:Environmental Health Perspectives
230:U.S. Food and Drug Administration
171:, describes the magnitude of the
2718:Minimum inhibitory concentration
1790:Dose Finding in Drug Development
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655:is the following formula, where
513:
34:
2658:WHO list of essential medicines
2151:Non-specific effect of vaccines
988:Dose is usually in milligrams,
45:needs additional citations for
2713:Antimicrobial pharmacodynamics
1891:Online Tool for ELISA Analysis
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169:exposure–response relationship
1:
2638:Functional analog (chemistry)
1380:Ceiling effect (pharmacology)
429:Death, loss of consciousness
366:Illumination/Light intensity
247:Example stimuli and responses
2191:Hill equation (biochemistry)
1497:Food and Drug Administration
1285:Shape of dose-response curve
628:Hill equation (biochemistry)
581:when in fact there is none.
69:"Dose–response relationship"
2779:
2706:Antimicrobial pharmacology
2186:Dose–response relationship
2116:Desensitization (medicine)
1736:Ding, S; Sachs, F (1999).
625:
205:stimulus response function
165:dose–response relationship
133:
2628:Coinduction (anesthetics)
1307:linear no-threshold model
640:. The Hill equation is a
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381:
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239:The dose makes the poison
2693:Multiple drug resistance
2666:Tolerance and resistance
2034:Physiological antagonist
698:{\displaystyle {\ce {}}}
532:10.1007/0-387-33706-7_10
2444:Neuropsychopharmacology
2206:Cheng-Prussoff Equation
2201:Del Castillo Katz model
2128:Other effects of ligand
2111:Receptor (biochemistry)
2029:Irreversible antagonist
1798:10.1007/0-387-33706-7_9
1695:Pharmacological Reviews
1442:(9 Part 1): 2973–2979.
209:stimulus response curve
2580:Classical pharmacology
2341:Plasma protein binding
2316:Volume of distribution
2024:Competitive antagonist
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524:is missing information
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2688:Antibiotic resistance
2480:Clinical pharmacology
1999:Physiological agonist
1959:Ligand (biochemistry)
1754:10.1085/jgp.113.5.695
1516:Altshuler, B (1981).
1385:Certain safety factor
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601:ligand binding assays
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352:Temperature receptors
300:Biochemical receptors
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2585:Reverse pharmacology
2495:Pharmacoepidemiology
2336:Biological half-life
2216:Ligand binding assay
2090:Activity at receptor
1984:Irreversible agonist
1856:10.1210/er.2011-1050
1400:Spatial epidemiology
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382:Pathogen dose (e.g.
374:Mechanical pressure
334:Allosteric modulator
201:dose–response curves
54:improve this article
2633:Combination therapy
2521:Pharmacoinformatics
2490:Medicinal chemistry
2096:Mechanism of action
1651:2015NatSR...514701V
1591:10.1021/es60130a004
1583:1977EnST...11..714H
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559:dose–response curve
407:
2603:Immunopharmacology
2553:Pharmacotoxicology
2454:Psychopharmacology
2246:Intrinsic activity
2146:Pleiotropy (drugs)
2067:Agonist-antagonist
1979:Endogenous agonist
1639:Scientific Reports
1536:10.1289/ehp.814223
1410:Dose fractionation
1341:. You can help by
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1844:Endocrine Reviews
1826:10.1038/srep11376
1807:978-0-387-29074-4
1707:10.1124/pr.55.4.4
1660:10.1038/srep14701
1405:Weber–Fechner law
1375:Arndt–Schulz rule
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752:{\displaystyle n}
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414:Example Response
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377:Mechanoreceptors
266:Example Stimulus
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16:(Redirected from
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2326:Rate of infusion
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411:System Level
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358:Sound levels
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71: –
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65:Find sources:
59:
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43:This article
41:
37:
32:
31:
19:
18:Dose-response
2648:Chemotherapy
2608:Cell biology
2509:Biochemistry
2433:Neuroscience
2381:Distribution
2311:Loading dose
2185:
1994:Superagonist
1951:Pharmacology
1905:Ecotoxmodels
1847:
1843:
1832:
1816:
1789:
1745:
1741:
1731:
1698:
1694:
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1645:(1): 14701.
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1577:(7): 714–9.
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586:probit model
583:
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476:Biochemistry
472:Cell biology
424:Epidemiology
310:Transporters
294:isoprenaline
250:
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218:
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168:
164:
162:
131:
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52:Please help
47:verification
44:
2733:Bactericide
2409:Compartment
2220:Patch clamp
2196:Schild plot
1297:Limitations
646:logit model
590:logit model
361:Hair cells
347:Temperature
326:propranolol
195:(usually a
110:August 2018
2763:Toxicology
2752:Categories
2613:Physiology
2545:Toxicology
2437:psychology
2386:Metabolism
2376:Absorption
2370:Liberation
2212:Organ bath
2140:Functional
2019:Antagonist
2012:Inhibitory
1967:Excitatory
1900:Calculator
1417:References
1350:April 2019
990:micrograms
597:organ bath
546:April 2023
439:Physiology
420:Population
397:intensity
317:Antagonist
260:deaths).
80:newspapers
2395:Clearance
2391:Excretion
2210:Methods (
1896:Online IC
1291:monotonic
1196:×
1059:×
575:sigmoidal
571:logarithm
567:endpoints
538:talk page
395:Radiation
222:organisms
2513:genetics
2485:Pharmacy
2472:Medicine
2282:Affinity
2241:Efficacy
2179:Analysis
2161:Toxicity
1874:22419778
1772:10228183
1715:14657418
1679:26424192
1530:: 23–7.
1464:(2009).
1390:Hormesis
1368:See also
435:Organism
322:ketamine
290:nicotine
197:chemical
193:stressor
189:stimulus
181:function
177:organism
173:response
2423:Related
2366:(L)ADME
2320:Initial
2304:Metrics
2251:Potency
2234:Metrics
2136:Binding
2106:Binding
1974:Agonist
1865:3365860
1763:2222910
1723:1729572
1670:4589737
1647:Bibcode
1579:Bibcode
1554:7333256
1545:1568781
939:potency
759:is the
534:§ 10.2.
305:Enzymes
285:Agonist
269:Target
187:) to a
179:, as a
156:of 0.7
147:agonist
94:scholar
2425:fields
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607:, and
458:Tissue
337:(e.g.
320:(e.g.
288:(e.g.
175:of an
96:
89:
82:
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67:
2622:Other
2359:LADME
1719:S2CID
1610:Wiley
1492:(PDF)
1471:(PDF)
1277:The E
1004:The E
1000:model
561:is a
454:Organ
448:data
279:Toxin
185:doses
167:, or
158:molar
101:JSTOR
87:books
2511:and
2435:and
2271:TD50
2267:LD50
2263:ED50
2259:IC50
2255:EC50
2057:Drug
1870:PMID
1802:ISBN
1768:PMID
1711:PMID
1675:PMID
1614:ISBN
1550:PMID
1444:PMID
1310:non-
651:The
468:Cell
400:n/a
389:n/a
281:dose
275:Drug
163:The
73:news
2368:: (
1860:PMC
1852:doi
1822:doi
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1758:PMC
1750:doi
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1655:doi
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1540:PMC
1532:doi
1345:.
1305:or
1279:max
1006:max
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588:or
528:doi
446:EEG
384:LPS
207:or
191:or
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867:1
862:=
854:n
850:]
846:A
843:[
840:+
835:n
816:n
812:]
808:A
805:[
799:=
791:x
788:a
785:m
780:E
776:E
747:n
719:C
716:E
691:]
688:A
685:[
663:E
548:)
544:(
540:.
530::
470:(
456:/
422:(
307:,
302:,
277:/
138:.
123:)
117:(
112:)
108:(
98:·
91:·
84:·
77:·
50:.
20:)
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