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• W2091277908 abstract "The pharmacokinetic behavior of medicines used in humans follows largely predictable patterns across the human age range from premature babies to elderly adults. Most of the differences associated with age are in fact due to differences in size. Additional considerations are required to describe the processes of maturation of clearance processes and postnatal changes in body composition. Application of standard approaches to reporting pharmacokinetic parameters is essential for comparative human pharmacokinetic studies from babies to adults. A standardized comparison of pharmacokinetic parameters obtained in children and adults is shown for 46 drugs. Appropriate size scaling shows that children (over 2 years old) are similar to adults. Maturation changes are generally completed within the first 2 years of postnatal life; consequently babies may be considered as immature children, whereas children are just small adults. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 102:2941–2952, 2013 The pharmacokinetic behavior of medicines used in humans follows largely predictable patterns across the human age range from premature babies to elderly adults. Most of the differences associated with age are in fact due to differences in size. Additional considerations are required to describe the processes of maturation of clearance processes and postnatal changes in body composition. Application of standard approaches to reporting pharmacokinetic parameters is essential for comparative human pharmacokinetic studies from babies to adults. A standardized comparison of pharmacokinetic parameters obtained in children and adults is shown for 46 drugs. Appropriate size scaling shows that children (over 2 years old) are similar to adults. Maturation changes are generally completed within the first 2 years of postnatal life; consequently babies may be considered as immature children, whereas children are just small adults. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 102:2941–2952, 2013 Medicines are used throughout the human age span. Arguably, the most critical decisions about the use of medicines, particularly those related to dose, are made in very premature infants. Medicines in that age group may be essential for life and treatment consequences, both beneficial and adverse, will literally be life-long. At the other end of the age spectrum the elderly adult is also a frequent consumer of medicines for a wide range of diseases. Rational dosing of medicines is based on the target concentration strategy.1.Sheiner L. Tozer T. Clinical pharmacokinetics: The use of plasma concentrations of drugs.in: Melmon K. Morelli H. Clinical pharmacology: Basic principles of therapeutics. Macmillan, New York1978: 71-109Google Scholar This strategy is especially valuable when trying to predict a suitable initial dose and when the response to treatment is difficult to observe to adjust the dose in an individual. The use of pharmacokinetic–pharmacodynamic principles and measured drug concentrations allows more precise tailoring of individual dose through target concentration intervention.2.Holford N.H. Target concentration intervention: Beyond Y2K.Br J Clin Pharmacol. 1999; 48: 9-13Crossref PubMed Scopus (66) Google Scholar Studies of medicines during drug development usually exclude the very young and very old so that prediction of a suitable dose must often be made by extrapolation in those populations. Empirical rules of thumb are widespread for dosing of babies and children and these often become the standard of care without any clear pharmacological justification. It has often been said quite uncritically that children are not small adults,3.Moore P. Children are not small adults.Lancet. 1998; 352: 630Abstract Full Text Full Text PDF PubMed Scopus (26) Google Scholar, 4.Bouzom F. Walther B. Pharmacokinetic predictions in children by using the physiologically based pharmacokinetic modelling.Fundam Clin Pharmacol. 2008; 22: 579-587Crossref PubMed Scopus (38) Google Scholar but this statement ignores the facts that explain most of the important differences in dose on the basis of size.5.Anderson B.J. Holford N.H. Mechanism-based concepts of size and maturity in pharmacokinetics.Annu Rev Pharmacol Toxicol. 2008; 48: 303-332Crossref PubMed Scopus (784) Google Scholar The pharmacokinetic component of the target concentration strategy has been the subject of widespread investigation and this has led to some insights that can be used to make more rational dosing decisions. Studies in the elderly have largely demonstrated that age itself is not an important predictor of pharmacokinetic behavior after other factors such as size, renal function (RF), disease, and drug interactions had been accounted for.6.Butler J.M. Begg E.J. Free drug metabolic clearance in elderly people.Clin Pharmacokinet. 2008; 47: 297-321Crossref PubMed Scopus (103) Google Scholar, 7.Polasek T.M. Patel F. Jensen B.P. Sorich M.J. Wiese M.D. Doogue M.P. Predicted metabolic drug clearance with increasing adult age.Br J Clin Pharmacol. 2013; 75: 1019-1028Crossref PubMed Scopus (41) Google Scholar On the contrary, there are substantial differences in drug distribution and elimination in the very young that are not predictable except on the basis of age which we describe in detail below. We argue that a standard approach to reporting pharmacokinetic parameters8.Holford N.H. A size standard for pharmacokinetics.Clin Pharmacokinet. 1996; 30: 329-332Crossref PubMed Scopus (415) Google Scholar is important to understand the differences and similarities between humans of all ages from babies to adults. Pharmacokinetics is primarily concerned with the time course of drug concentration in the body. Sometimes only the steady state concentration is of interest and then time is no longer of relevance. The time course of concentration is determined by three processes: input, distribution, and elimination. These processes are more general than the traditional “absorption, distribution, metabolism, excretion” description because input processes are more than just absorption and elimination covers both metabolism and excretion. Drug input is characterized by two separate components—the rate of input and the extent of input (bioavailability). Nonparenteral input processes (absorption) are subject to pathophysiological differences in the young and the elderly. Intestinal transit increases quite rapidly after birth reaching adult values around 7 months.9.Gupta M. Brans Y. Gastric retention in neonates.Pediatrics. 1978; 62: 26-29PubMed Google Scholar, 10.Grand R.J. Watkins J.B. Torti F.M. Development of the human intestinal tract: A review.Gastroenterology. 1976; 70: 790-810Abstract Full Text PDF PubMed Scopus (367) Google Scholar, 11.Liang J. Co E. Zhang M. Pineda J. Chen J.D. Development of gastric slow waves in preterm infants measured by electrogastrography.Am J Physiol. 1998; 274: G503-G508PubMed Google Scholar, 12.Carlos M.A. Babyn P.S. Marcon M.A. Moore A.M. Changes in gastric emptying in early postnatal life.J Pediatr. 1997; 130: 931-937Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar Decreased acidity in the neonate (pH>4)13.Rodbro P. Krasilnikoff P.A. Christiansen P.M. Parietal cell secretory function in early childhood.Scand J Gastroenterol. 1967; 2: 209-213Crossref PubMed Scopus (26) Google Scholar, 14.Agunod M. Yamaguchi N. Lopez R. Luhby A.L. Glass G.B. Correlative study of hydrochloric acid, pepsin, and intrinsic factor secretion in newborns and infants.Am J Dig Dis. 1969; 14: 400-414Crossref PubMed Scopus (230) Google Scholar may change the extent of absorption of orally administered medicines.15.Huang N.N. High R.H. Comparison of serum levels following the administration of oral and parenteral preparations of penicillin to infants and children of various age groups.J Pediatr. 1953; 42: 657-658Abstract Full Text PDF PubMed Scopus (64) Google Scholar Bioavailability, relative to adults, may be increased due to immaturity of enteral enzyme and transporter systems.16.Suchy F.J. Balistreri W.F. Heubi J.E. Searcy J.E. Levin R.S. Physiologic cholestasis: Elevation of the primary serum bile acid concentrations in normal infants.Gastroenterology. 1981; 80: 1037-1041PubMed Google Scholar, 17.Poley J.R. Dower J.C. Owen Jr., C.A. Stickler G.B. Bile acids in infants and children.J Lab Clin Med. 1964; 63: 838-846PubMed Google Scholar Behavioral immaturity may also impact on drug input in preschool children. Alternative nonparental delivery routes such as buccal and sublingual absorption is unsatisfactory in children because prolonged exposure to the mucosal surface is often required. Younger children find it difficult to comply with instructions to hold drugs in their mouths for the requisite retention time (particularly if taste is unfavorable), and this results in more drug swallowed or expectorated than in adults.18.Karl H.W. Rosenberger J.L. Larach M.G. Ruffle J.M. Transmucosal administration of midazolam for premedication of pediatric patients. Comparison of the nasal and sublingual routes.Anesthesiology. 1993; 78: 885-891Crossref PubMed Scopus (161) Google Scholar, 19.Wheeler M. Birmingham P.K. Dsida R.M. Wang Z. Cote C.J. Avram M.J. Uptake pharmacokinetics of the Fentanyl Oralet in children scheduled for central venous access removal: Implications for the timing of initiating painful procedures.Pediatr Anesth. 2002; 12: 594-599Crossref PubMed Scopus (39) Google Scholar In general absorption rate is slower in both the elderly and the very young than in nonelderly adults. The effect of slower absorption is generally beneficial because it reduces peak to trough variability in concentration and may reduce adverse effects associated with higher peak concentrations. For some special indications such as acute pain relief, lower peak concentrations may be a disadvantage. Overall however, differences in absorption are largely difficult to predict in individuals at any age. These differences have minimal impact on maintenance dosing decisions and are not central to the application of pharmacokinetics to target concentration intervention. Theory based allometry predicts that volume of distribution will be linearly related to body weight (assuming normal body composition) (Eq. 1).Fsize=WTWTSTD1V=VSTD×Fsize(1) V is the volume of distribution predicted in a subject of WT kg based on a standard individual VSTD whose size is WTSTD. WTSTD is ideally set to 70 kg to compare volume estimates across different studies.8.Holford N.H. A size standard for pharmacokinetics.Clin Pharmacokinet. 1996; 30: 329-332Crossref PubMed Scopus (415) Google Scholar The theory based allometric exponent for volume is 1 which means that volume is linearly proportional to weight.5.Anderson B.J. Holford N.H. Mechanism-based concepts of size and maturity in pharmacokinetics.Annu Rev Pharmacol Toxicol. 2008; 48: 303-332Crossref PubMed Scopus (784) Google Scholar There are few clear examples of differences in apparent volume of distribution associated with age. In the elderly it has been noted that the volume of distribution after accounting for differences in body weight is larger with a corresponding increase in half-life of diazepam.20.Klotz U. Avant G.R. Hoyumpa A. Schenker S. Wilkinson G.R. The effects of age and liver disease on the disposition and elimination of diazepam in adult man.J Clin Invest. 1975; 55: 347-359Crossref PubMed Scopus (525) Google Scholar The reason for this increase may be due to altered body composition with more fatty tissue per kg in the elderly. Increased fat would allow greater partition into these tissues and increase the apparent volume of distribution. At birth both total body water and extracellular water (expressed per kg) are substantially larger than adults, but this excess water acquired during gestation in the amniotic fluid “swimming pool” is lost rapidly in the first few months of life followed by a slower approach to adults values around puberty.21.Friis-Hansen B. Body water compartments in children: Changes during growth and related changes in body composition.Pediatrics. 1961; 28: 169-181PubMed Google Scholar Apparent volume of distribution of drugs similar to extracellular fluid volume, such as the aminoglycosides, falls from 0.44 L/kg to adult values (0.25 L/kg) during the 3 months after birth.22.Kelman A.W. Thomson A.H. Whiting B. Bryson S.M. Steedman D.A. Mawer G.E. Samba-Donga L.A. Estimation of gentamicin clearance and volume of distribution in neonates and young children.Br J Clin Pharmacol. 1984; 18: 685-692Crossref PubMed Scopus (49) Google Scholar Drugs with larger volumes of distribution, such as paracetamol, may also show a rapid decrease in V over the first week of neonatal life.23.Anderson B.J. van Lingen R.A. Hansen T.G. Lin Y.C. Holford N.H. Acetaminophen developmental pharmacokinetics in premature neonates and infants: A pooled population analysis.Anesthesiology. 2002; 96: 1336-1345Crossref PubMed Scopus (188) Google Scholar This is largely explicable in terms of the known changes in water volumes in the body.21.Friis-Hansen B. Body water compartments in children: Changes during growth and related changes in body composition.Pediatrics. 1961; 28: 169-181PubMed Google Scholar The volume of distribution may be lower at birth compared with adults, for example, the apparent volume of distribution of morphine in neonates is about 25% lower than in adults and approaches adult values with a maturation half-life of 20 days.24.Holford N.H. Ma S.C. Anderson B.J. Prediction of morphine dose in humans.Paediatr Anaesth. 2012; 22: 209-222Crossref PubMed Scopus (54) Google Scholar The reason for the increase in volume of distribution is unknown but may be related to changes in body composition and distribution to tissue binding sites. Once early postnatal changes have taken place apparent volumes are similar across all age groups. The most striking pharmacokinetic differences between babies and adults are in elimination clearance. These differences arise for two distinct and biologically different reasons—size and maturation. Table 1 reviews a wide range of different drugs whose pharmacokinetics have been described in children in terms of size and maturation.Table 1Summary of Models Describing Size and Maturation and Standardized Pharmacokinetic Parameters in Children and AdultsDrugReference for ChildrenReference for AdultSizeMaturationCL ChildCL AdultCLchild/ CLadultTM50HillFmat 40VchildVadultVchild/ VadultFchildFadultAcyclovir2526TBA (BSA)Smax23.821.91.0858.06.179%44.348.30.920.121Amikacin2726TBAExponential49.45.469.0527%31.718.91.6811Amphotericin2826TBANone9.661.935.0047%36.853.20.6911Busulfan2926TBANone10.213.20.7736.848.510.70Busulfan3026TBASmax12.513.20.9445.72.342%49.748.51.0210.70Carvedilol3126TBAPower34.2136.50.941381051.310.251Ciprofloxacin3226TBANone29.931.90.942601541.690.71Clonidine3326TBASmax13.713.01.054.06 PNA days0.44165%3521472.390.91Clonidine3426TBAAsymptotic exponential14.613.01.1226%181.501471.2311Cyclosporin3526TBANone21.623.90.901583150.500.361Dexmedetomidine3637TBASmax42.144.80.9444.52.5643%1251131.1111Diclofenac3826TBANone16.517.60.9415.011.91.2611Fluconazole3926TBAPower3.381.132.986%71.742.01.7111Gentamicin4026TBAPower3.565.380.6628.321.71.3011GFR41TBASmax7.2647.73.435%Indometacin4226TBAExponential0.155.880.0315.920.30.7811Itraconazole4326TBANone19.521.40.916727490.900.551Ketamine4445TBALinear83.171.41.161302170.600.451Ketorolac4626TBANone2.052.100.9815.2014.71.0311Lamivudine4748TBASmax25.420.61.24593.0224%1681051.600.820.82Leviteracetam4926TBASmax4.094.031.02542.532%37.049.00.760.921Levobupivacaine5051TBASmax22.139.00.5735.73.8161%18967.02.8211Lopinavir5226TBAEmax5.875.041.1639.7150%92.042.02.19Unknown UnknownMelatonin5354TBANone0.5758.00.009859.763.00.9511Midazolam5526TBASmax31.427.71.1373.6314%11977.0Not reported11Morphine2426TBASmax86.41010.8658.13.5821%2502311.0811Ondansetron5626TBAAsymptotic exponential37.024.81.4916%2421331.8211Pantoprazole5726TBASmax8.3111.80.7147.991.4843%13.311.91.1211Paracetamol5826TBASmax16.221.00.7752.23.4329%63.266.50.950.8621Paracetamol5926TBANone16.521.00.7941.766.50.6311Penciclovir6061TBASmax35.731.21.1489.93.874%91.983.11.110.601Phénobarbital6226TBAEmax2.660.2610.2043.16148%64.937.81.7211Propofol6326TBASmax1041130.9238.54.654%2581192.1711Propofol6426TBANone1351131.19851190.7111Propofol6526TBANone861130.763201192.6811Pyrimethamine6667TBASmax1.271.440.8845.437.3928%286161.001.78Unknown UnknownQuinine6826TBANone3.175.880.5487.21190.7311Ranitidine6926TBANone32.143.70.7335%285913.1311Remifentanil7026TBANone1781890.9494%16.328.00.5811Ropivacaine7172TBASmax6674421.5166.24.549%35420.8311Sulfadoxine6667TBASmax0.050.041.2238.714.0753%24.209.802.47UnknownUnknownTacrolimus7326TBANone2.923.780.7721563.73.380.211Tamsulosin7426TBANone1.822.600.7030.014.02.140.81Tenofovir7526TBANone15.010.91.3726342.06.260.251Thiopentone7676TBAExponential7.18E+5512.845.59E+5443%142981.4511Tobramycin7778TBANone6.378.480.7520.026.60.7511Tramadol7926TBAAsymptotic exponential24.033.60.7173%1811890.9611Valdecoxib8081TBASmax8.607.381.1688.838%62.086.00.7211Vancomycin8226TBASmax3.795.660.6733.366%39.427.31.4411Clearance estimates are L/h and volume estimates (steady state) are L per 70 kg. Biovailability (F) used to calculate parameters reported after nonparenteral dosing. When F was unknown it was assumed to be the same for both children and adults. TM50=maturation half-life weeks, hill=the slope parameter for the sigmoid Emax model, TBA=theory-based allometry, EA=empirical allometry with estimates not significantly different from theory, Smax=sigmoid Emax maturation, BSA=body surface area. Open table in a new tab Clearance estimates are L/h and volume estimates (steady state) are L per 70 kg. Biovailability (F) used to calculate parameters reported after nonparenteral dosing. When F was unknown it was assumed to be the same for both children and adults. TM50=maturation half-life weeks, hill=the slope parameter for the sigmoid Emax model, TBA=theory-based allometry, EA=empirical allometry with estimates not significantly different from theory, Smax=sigmoid Emax maturation, BSA=body surface area. The most obvious difference between babies and adults is body size that may range from 0.5 kg in a very premature neonate to over 250 kg in a large adult. The 500-fold difference in weight does not directly translate into the same range of differences in clearance. Theory based allometry predicts that a 500-fold difference in weight is expected to produce only a 100-fold difference (Eq. 2) in a functional property of the body such as clearance.83.West G.B. Brown J.H. Enquist B.J. A general model for the origin of allometric scaling laws in biology.Science. 1997; 276: 122-126Crossref PubMed Scopus (3431) Google Scholar, 84.Savage V.M. Gillooly J.F. Woodruff W.H. West G.B. Allen A.P. Enquist B.J. Brown J.H. The predominance of quarter-power scaling in biology.Funct Ecol. 2004; 18: 257-282Crossref Scopus (471) Google ScholarFsize=WTWTSTD3/4CL=CLSTD×Fsize(2) Fsize expresses the allometric size relative to a standard weight WTSTD. CL is the clearance predicted in a subject of WT kg based on a standard individual CLstd whose size is WTstd. The theory based allometric exponent for clearance is 3/4, which means that clearance increases more slowly as weight increases.8.Holford N.H. A size standard for pharmacokinetics.Clin Pharmacokinet. 1996; 30: 329-332Crossref PubMed Scopus (415) Google Scholar Figure 1 shows how allometric size predicted using a theory based exponent of 3/4 for clearance varies across the human weight range. Allometric scaling is the most widely used method to describe size differences. In almost all cases theory based allometric exponents are used. The wide imprecision of empirical estimates of allometric exponents when estimated from typical sized datasets with limited numbers of subjects and distribution of weights means that empirical exponents are of little predictive value.5.Anderson B.J. Holford N.H. Mechanism-based concepts of size and maturity in pharmacokinetics.Annu Rev Pharmacol Toxicol. 2008; 48: 303-332Crossref PubMed Scopus (784) Google Scholar This was demonstrated by an external evaluation of models for predicting morphine clearance24.Holford N.H. Ma S.C. Anderson B.J. Prediction of morphine dose in humans.Paediatr Anaesth. 2012; 22: 209-222Crossref PubMed Scopus (54) Google Scholar which showed that all empirical allometric models had unacceptable predictions. Allometry alone may be adequate to describe the clearance of a drug such as the opioid remifentanil that is cleared by plasma esterases that are mature at birth.85.Welzing L. Ebenfeld S. Dlugay V. Wiesen M.H. Roth B. Mueller C. Remifentanil degradation in umbilical cord blood of preterm infants.Anesthesiology. 2011; 114: 570-577Crossref PubMed Scopus (39) Google Scholar However, clearance is immature at birth for most drugs. The second, less striking, but nevertheless well recognized difference between babies and adults is the maturation of body function during both intrauterine and extrauterine life. Unlike the relationship between size and clearance there is no underlying theory to predict how clearances changes during development. Development is usually defined in relation to age. The start of the biological clock occurs at the moment of conception and consequently postconception age is the most appropriate to describe the biology of clearance maturation. The biological age is determined by the time of conception. In most cases, this is not known so it is recommended that the age since the last menstrual period be used because this is thought to be more reliable and consistent for determining biological age.86.Engle W.A. Age terminology during the perinatal period.Pediatrics. 2004; 114: 1362-1364Crossref PubMed Scopus (334) Google Scholar This postmenstrual age is on average around 2 weeks longer than postconception age but it is widely used to determine gestational age. Gestational age is the age at birth. It does not change after that. If gestational age and postnatal age are known then postmenstrual age can be predicted by adding gestational age to postnatal age. Neither gestational age nor postnatal age is a good predictor of maturation. The instant of conception and formation of a single cell defines the start of maturation of the living organism. A single cell has enzymes capable of modifying drug molecules (e.g., esterases) and thus in principle the elimination clearance of some drugs is not zero. Other drugs which are typically excreted in the urine cannot be eliminated until the kidney develops around 16 weeks after conception.87.Chen N. Aleksa K. Woodland C. Rieder M. Koren G. Ontogeny of drug elimination by the human kidney.Pediatr Nephrol. 2006; 21: 160-168Crossref PubMed Scopus (106) Google Scholar As the fetus develops and functional organs are formed there is a corresponding increase in clearance mechanisms. On the basis of the postnatal studies including premature and mature neonates it appears that glomerular filtration rate develops rather slowly and only reaches about 25% of adult values around the time of normal full term gestation (40 weeks postmenstrual age). It then accelerates quite rapidly reaching about 50% of adult values at 48 weeks postmenstrual age.41.Rhodin M.M. Anderson B.J. Peters A.M. Coulthard M.G. Wilkins B. Cole M. Chatelut E. Grubb A. Veal G.J. Keir M.J. Holford N.H. Human renal function maturation: A quantitative description using weight and postmenstrual age.Pediatr Nephrol. 2009; 24: 67-76Crossref PubMed Scopus (340) Google Scholar There are further increments in glomerular filtration rate occurring during the first few weeks after birth but these are relatively small compared with birth independent maturation processes.88.Anderson B.J. Holford N.H.G. Tips and traps analyzing pediatric PK data.Pediatr Anesth. 2011; 21: 222-237Crossref PubMed Scopus (120) Google Scholar Age is nothing more than a measure of time, which is itself not an explanation of the state of maturation but is simply a convenient covariate associated with development. Maturation must therefore be defined empirically as a function of time. Four potential candidates for empirical models to describe maturation are shown in Figure 2. All the models reach the mature adult value before a postmenstrual age of 20 years. The linear model is simple and may be useful to describe data covering a small range of ages but does not describe the slow early development and rapid later phases. The exponential model does better at describing the slow early phase but then increases too rapidly and quickly exceeds the mature adult value. The asymptotic exponential model is biologically plausible at its extremes (around 0 when postmenstrual age is 0 and approaching the adult value of 100 during the first few years of life. It tends to rise too rapidly in earlier years, which is where the sigmoid Emax maturation model shows particular merit. The steepness of the rapid maturation phase around the time of normal full term gestation allows a slow early phase and asymptotic approach to the adult value. The sigmoid Emax maturation function is shown in Eq. 3. Fmat is the fraction of the adult value of clearance. PMA is postmenstrual age (usually in weeks) and TM50 is the value of PMA when maturation reaches 50% of the adult clearance.Fmat=11+PMATM50−Hill(3) The maturation models shown in Table 1 largely use the sigmoid Emax model. Some earlier studies used other functions but the flexibility of the sigmoid Emax model provides a practical way of comparing rates of maturation with different drugs. Because the estimates of maturation parameters are dependent on assumptions made about size it is important to use a consistent approach to compare results from different studies and with different drugs. Clearance may be predicted by combining an allometric model for size with a maturation function as shown in Eq. 4.CL=CLSTD×Fsize×Fmat(4) For the purposes of this discussion, we have assumed that allometric size is adequately predicted from total body weight. This simplification does not account for differences in body composition, for example, associated with obesity. The use of normal fat mass should be considered to account for the partitioning of weight into fat free and fat mass components.89.Anderson B.J. Holford N.H.G. Mechanistic basis of using body size and maturation to predict clearance in humans.Drug Metab Pharmacokinet. 2009; 24: 25-36Crossref PubMed Scopus (358) Google Scholar The combined effects of weight and age on clearance are shown in Figure 3. Weight appropriate for age was predicted based on a large dataset of weight and age values.90.Sumpter A.L. Holford N.H.G. Predicting weight using postmenstrual age—Neonates to adults.Pediatr Anesth. 2011; 21: 309-315Crossref PubMed Scopus (34) Google Scholar Clearance predicted from weight alone can be compared with clearance that also takes into account maturation. The parameters are those typical for glomerular filtration rate41.Rhodin M.M. Anderson B.J. Peters A.M. Coulthard M.G. Wilkins B. Cole M. Chatelut E. Grubb A. Veal G.J. Keir M.J. Holford N.H. Human renal function maturation: A quantitative description using weight and postmenstrual age.Pediatr Nephrol. 2009; 24: 67-76Crossref PubMed Scopus (340) Google Scholar but the differences in shape due to maturation are not markedly different with other examples of maturation. It can be seen that clearance increases approximately linearly with postnatal age after the age of 2 years. Before 2 years of age there is a more rapid increase in clearance reflecting both an increase in size and maturation. At full term (40 weeks postmenstrual age) the maturation is about 35% of the expected adult value when size is accounted for using weight. Figure 3 also shows the commonly observed higher clearance in children (especially infants) when expressed per kg. The higher clearance per kg relative to adults is explained by allometric theory. The use of a per kg scale for clearance instead of theory based allometry gives rise to the false conclusion that clearance is higher in children than in adults. In fact, clearance is obviously smaller than adults at all ages but the use of over simple scaling method is misleading. It is important to note that only allometric theory provides a sound explanation for the higher clearance per kg in children. Other explanations based on liver volume or cardiac output91.Anderson B.J. McKee A.D. Holford N.H.G. Size, myths and the clinical pharmacokinetics of analgesia in paediatric patients.Clin Pharmacokinet. 1997; 33: 313-327Crossref PubMed Scopus (170) Google Scholar are in fact just empirical associations of organ structure and function which do not actually explain why structure and function vary in the way they do as body weight increases. Other empirical approaches to describe changes of clearance with weight and/or age have been reported but are not so widely used as allometry combined with the sigmoid maturation model. An empirical allometric method using a function of weight to predict the allometric exponent has been reported for propofol.92.Wang C. Peeters M.Y. Allegaert K. van Oud-Alblas H.J. Krekels E.H. Tibboel D. Danhof M. Knibbe C.A. A bodyweight-dependent allometric exponent for scaling clearance across the human life-span.Pharm Res. 2012; 29: 1570-1581Crossref PubMed Scopus (64) Google Scholar Attempts to use the same kind of model to describe morphine had poor predictive properties when applied to an external evaluation dataset24.Holford N.H. Ma S.C. Anderson B.J. Prediction of morphine dose in humans.Paediatr Anaesth. 2012; 22: 209-222Crossref PubMed Scopus (54) Google Scholar that suggest this approach may not ha" @default.
• W2091277908 created "2016-06-24" @default.
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• W2091277908 date "2013-09-01" @default.
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• W2091277908 title "A Pharmacokinetic Standard for Babies and Adults" @default.
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