Influence of Variables in Drug Absorption and Their Implications

Hemant  Joshi

Hemant Joshi

Prediction of drug absorption has become a powerful tool for the development
of medicinal drugs and several attempts have focused on predicting drug absorption
from structure. However, there seem to exist several misperceptions towards
nature of absorption. Among those are intestinal absorption,
permeability, fraction absorbed, and bioavailability, to some extent. In fact
the these terms can neither be regarded as equivalents nor can be used

Further the terms absorption; permeability, etc. are specific, discrete,
fundamental properties of a drug molecule which cannot be solely predicted from
its structural representation but in real status, drug absorption
is a very complex process and depends upon various properties
of molecule such as solubility and permeability,formulation aspects,
physiological variables, including regional permeability differences
(absorption window), pH, lumenal and mucosal enzymatic activity, intestinal
motility, etc. An attempt is been made in order to explore and understand the influence of the above said variables on drug absorption and
their implications in developing predictive absorption algorithms.


Significant recent interest has focused on predicting oral drug absorption from drug structure1-7. However several misperceptions pertaining to the nature of absorption seem to be common in many approaches. Among those are intestinal absorption, permeability, fraction absorbed, and, in some cases, and bioavailability to certain extent are all equivalent and can be used interchangeably. In reality, these terms are discrete and unique, and are different processes and are related to drug structure in different ways. An instance of failure to encourage these differences by means of a model parameter, like permeability towards prediction of bioavailability, which is actually dependent upon several other parameters such as permeability, solubility, and first-pass metabolism. Another aspect dealing with absorption, permeability, fraction absorbed etc. being taken up as fundamental properties of a molecule and can be predicted directly from its structure, which many a times is untrue.  Drug absorption as such is a very complex process that manifests itself through potential interaction with a host of physicochemical such as solubility, partition coefficient, molecular size, dissociation constant etc and physiological variables which include presystemic metabolism/efflux, regional permabilities (absorption window) along the gastrointestinal tract pH, luminal and mucosal enzymology, intestinal motility, disease states, demographics (gender, age, ethnicity), and biopharmaceutical classification of solid dosage forms. The present work describes an attempt been made in order to explore and understand the influence of the above said variables on drug absorption and their implications in developing predictive drug absorption algorithms.

Bioavailability, used to describe the fraction of an administered dose of medication that reaches the systemic circulation, one of the principal pharmacokinetic properties of drugs. The bioavailability of a drug (F) after oral administration is described in general by the following relationship.

F = fa x fg x fh . . .


fa, fg, fl are the fractions of unchanged drug absorbed

fa is an escape irreversible elimination as a drug passes sequentially from the gastrointestinal tract, across the gut wall fg, and traverses the liver fl into the systemic circulation8.

Thus, bioavailability can either be equal to or less than the fraction absorbed depending upon the extent of metabolism and loss during absorption process.

A schematic representation of relationships of a number of combinations of absorption and metabolism are shown in Figure 1, where as Figure 2 and 3, clearly depicts poor blood levels of a drug, which could be attributed to either poor absorption or good absorption associated with extensive metabolism8.




Figure 1, depicts a typical case where in metabolism and other means of drug loss are minimum and hence the concentration of parent drug approaches that of total drug related materials (TDRM), (TRDM is explained as a combination of parent drug and its metabolites formed).  The observations from Figure 2 describes a case where absorption is high due to which one can expect a high circulating levels of TDRM, but since metabolism is significant, parent drug concentrations are low. A typical case of low absorption and corresponding low metabolism shows poorer levels of TRDM as shown in Figure 3.

As described in Figure 2 and 3 it can be noted that a low circulating parent
drug levels could be either due to poor absorption or good absorption followed
by extensive metabolism. Historically metabolism during absorption was has been
thought to occur primarily in the liver, however the contribution of the intestinal
mucosa to first-pass metabolism have been recognized increasingly9

In order to understand Bioavailability two features are basically looked at
one being the fraction absorbed and the unchanged amount of drug that enters
systemic circulation, which measures of the extent of absorption. On the contrary
permeability could be related to understand rate of absorption.

J = Pe x SA x Δ C

Where J, represents the absorption flux, which is equal to permeability across intestinal mucosa generally expressed as amount / unit cross-sectional area/unit time; S, represents the surface area available for absorption and Δ C, is a concentration gradient across the mucosa10.

Factors that can influence permeability characteristics include structural characteristics, such as size, shape, charge, etc., drug formulation components, and intestinal pH. Factors affecting the concentration gradient include intrinsic solubility of the drug, formulation components, solid-state properties, etc.

A fundamental relationship between the rate measured as a permeability coefficient and the extent of absorption has been cited in theoretical grounds11. The citation has encouraged in an increase usage of in vitro permeability models, such as the HT-29, MDCK, Caco-2 cell monolayer system, to serve as an experimental surrogate for predicting oral absorption and identifying potential candidates in a drug discovery setting12, 13.  Although in some cases it has been possible to directly correlate absorption with permeability14, there are some instances where a poor correlation (shown in Figure 2) was observed for a series of synthetic antibacterial agents.

In continuation with the similar observations a research presents, rat bioavailability being compared with Caco-2 cell permeability coefficients for a series of oxazolidinone antibiotics15. The compounds have least or minimal metabolism and since bioavailability is a function of both absorption and metabolism, and to a first approximation (Figure 1), the fraction absorbed and bioavailability are equivalent15.  The observations suggested a poor correlation among oral bioavailability and Caco-2 permeability (Figure 4  A)15. Common aspect among the low-permeability well absorbed solutes is high solubility, for example, Eperezolid has aqueous solubility of 4.2 mg/ml. In contrast, the low-permeability poorly absorbed solutes have solubilities generally less than 0.05 mg/ml. However, Figure 4 B shows better correlation if both Caco-2 cell permeability and aqueous solubility, which are used to predict fraction of dose, absorbed, adopting the maximum absorbable dose model. Further observations showed that these oxazolidinones share characteristics of high Caco-2 cell permeability but very limited aqueous solubility15 (less than 0.01 mg/ml).


A simple model for interrelating permeability and solubility of a drug to estimate
absorption potential is maximum absorbable dose (MAD)16.
Briefly, MAD calculates the total mass of a drug that could be theoretically
absorbed if a saturated solution of the compound with solubility
S in the small intestinal volume (SIV) were absorbed with a first-order
rate constant, Ka, for a time equivalent to the small
intestinal transit time (SITT):

MAD = S x Ka x SIV x SITT

Using this model parameterized for the rat and comparing the predicted MAD
to dose actually administered, the relationship shown in Figure 4
B was found. Clearly, better prediction of absorption is achieved
when solubility, permeability, and dose are taken into consideration compared
with the situation obtained with a single parameter correlation.

Permeability based on structure relationships

Permeability is still an important aspect, if not the solely regarded as determinant of absorption, but still remains  informative to explore mechanisms contributing to permeability have encouraged the development of structure-based computational models of this property. To reach the systemic circulation, a drug must move from the intestinal lumen through an unstirred water layer and mucus coat adjacent to the epithelial cell surface. Movement across the epithelial layer takes place by two independent routes transcellular flux (i.e., movement across the cell) and paracellular flux, or movement between adjacent epithelial cells, restricted by the presence of tight junctions between the cells17, 18,19. The solute then encounters a basement membrane, interstitial space, and mesenteric capillary wall in accessing the mesenteric circulation. Any and all of these microenvironments can be considered as a resistance to solute movement with an associated permeability coefficient. Thus, the overall process consists of a number of resistances (reciprocal permeabilities) in series10. Furthermore, the influence of drug structure with permeability in these different domains will be different. For example, unstirred water layer permeability is inversely related to solute size, whereas paracellular permeability is dependent upon both size and charge. In the latter case, the characteristics of the paracellular "pore" result in size resisted diffusion as the size of the solute approaches that of the paracellular space. Furthermore, cations are more permeable than neutral species, which in turn are more permeable than anions, consistent with the negative charge characteristics of the paracellular space20, 21.

With respect to transcellular permeability, the relationship of solute structure with permeability depends upon the mechanism involved. Historically, a passive diffusion pathway has been assumed for most solutes. Nevertheless, an increasing number of active absorptive and secretory processes in intestinal epithelial cells are being identified for which many common drugs are substrates 22. Although active transport involves specific interactions between solute and transporter, passive diffusion is dependent upon solute partitioning into the cellular plasma membrane and diffusion coefficient within the membrane 19. Both these processes are influenced by the physicochemical and structural characteristics of the drug. Factors influencing plasma membrane partitioning are solute size, lipophilicity, hydrogen bonding potential, and charge characteristics, whereas diffusion is dependent upon size or total molecular surface area properties23. In general, nonpolar surface area favors partitioning, whereas polar hydrogen bonding functionality do not favor partitioning. With respect to diffusion, an inverse relationship with size is found, similar to the situation with paracellular permeability.

These multiple influences on permeability are manifested in a number of different ways. If intestinal permeability of a number of homologous nonactively transported solutes is measured as a function of membrane partitioning or, more commonly, an organic solvent partition coefficient as a surrogate, a sigmoidal relationship is frequently observed10, 24. For solutes with little or no membrane affinity, permeability is low, resulting primarily from paracellular diffusion of the solute. As the propensity of the solute to partition into the cell membrane increases, permeability also increases as a result of the significant increase in surface area of the transcellular pathway relative to the paracellular route. This increase in permeability will approach a plateau value beyond which further increases in partition coefficient do not result in increased permeability. This is the so-called aqueous boundary layer-limited situation where diffusion across the cell is very rapid relative to diffusion of the solute through the unstirred water/mucus layer adjacent to the cell25. Perturbing hydrodynamics that shift the plateau to a new limiting permeability can modify the dimensions and resistance of this layer.

In the case of ionizable solutes, permeability is also pH-dependent. The neutral uncharged species is capable of transcellular passive diffusion, whereas the charged species is restricted to the paracellular pathway. Thus, the observed permeability of such molecules is dependent upon the relative concentrations of charged and neutral species. In the case of a weak acid, such as salicylic acid, at pH less than about 5.5, rat intestinal permeability is aqueous boundary controlled. Increasing pH results in progressively lower permeability coefficients. At pH greater than 9, a limiting small permeability is achieved that is independent of further pH increases. This limiting permeability represents the paracellular diffusion of the charged anion10.

For solutes that are substrates for active uptake or efflux transporters, the relative contribution of the active pathway will depend upon the concentration of the solute in the lumen. For example, lisinopril is absorbed by a peptide transporter in the mucosa and shows progressively lower absorptive permeability in rat intestine as the concentration of drug is increased26. Other drugs that have been shown to be substrates for absorptive transporters include L-DOPA, baclofen, and melphalan (large neutral amino acid transporters), cephalasporin and -lactam antibiotics (oligopeptide transporters), pravastatin (monocarboxylic acid transporters), forscarnet and fosfomycin (phosphate transporters) 22.

More recently, the role of efflux transporters in influencing the permeability and overall bioavailability of drugs has gained considerable attention. Among these transporters is P-glycoprotein (P-gp) expressed on the apical surface of normal intestinal mucosa. In contrast to absorptive transporters that increase uptake of substrates from the intestinal lumen, P-gp impedes uptake by returning a portion of drug entering the mucosa back to the lumen in a concentration-dependent manner. An increasing number of drugs have been shown to be substrates for P-gp, including human immunodeficiency virus-protease inhibitors27  and verapamil28, where intestinal permeability is increased as lumen concentration increases. The potential for such P-gp-mediated intestinal efflux to provide a source of drug-drug interactions affecting oral bioavailability is also increasingly being described29. Given the importance of P-gp in drug absorption, attempts have been made to develop structure-transport relationships with varying degrees of success 30. One of the more promising models argues for the importance of hydrogen bonding acceptor groups in the substrate with a specific spatial orientation31. It must be noted that P-gp is only one of an emerging number of efflux transporters, including the multidrug resistance-associated proteins and breast cancer-resistance protein present in the intestinal mucosa and thought to influence drug permeability and bioavailability.

Briefly, intestinal permeation of a solute is a complex process containing
contributions from a number of pathways, the relative importance
of which depend upon the nature of the solute. Each of these pathways
will be dependent upon the structure of the solute, but in different
ways. The complexity of this property will make it very challenging
to develop simple, global, structure-based predictive models of intestinal

Solubility Factors

Both rate and extent of absorption can be significantly influenced by the intestinal solubility characteristics of the drug in the intestinal lumen. Frequently, these solubility characteristics may be optimized by the formulation scientist in ways that can have profound influences on the absorption of the drug in vivo. In the case of highly crystalline drugs, for example, aqueous solubility of the most stable crystal form generally is fairly low, resulting in poor absorption characteristics. Formation of higher energy, lower melting crystal forms or amorphous solids frequently yields more rapidly dissolving and higher solubility forms of the drug 32. The experimental human immunodeficiency virus-protease inhibitor PNU-103017, for example, has intrinsic solubility of about 1 to 3 µg/ml. Dosed as a conventional suspension to dogs; absorption was slow, giving very low blood levels. PNU-103017 is weakly acidic (pKa values about 6 and 9) and dosing as a pH 10 solution gives significantly more rapid and extensive absorption. When an amorphous suspension of the drug was dosed, absorption characteristics were similar to the solution, consistent with much more rapid dissolution and higher solubility of the amorphous material compared with the crystalline solid 33.

Similar enhancements in absorption of high-permeability low-solubility drugs can be achieved through particle size reduction. The resulting increase in surface area enhances dissolution characteristics and can frequently result in significantly improved absorption. Danazol is a poorly water-soluble drug (10 µg/ml) that shows poor absorption in human. Administration of a conventional suspension of danazol to dogs results in similar poor absorption (Cmax, 0.2 ± 0.06 µg/ml; 5.1 ± 1.9% bioavailability). Reducing particle size to an average of 85 nm (nanoparticle dispersion) profoundly increases absorption to give a Cmax of 3.01 ± 0.8 µg/ml and bioavailability of 82.3 ± 10.1% 34.

Ionizable drugs can exhibit significant differences in absorption properties
dependent upon the pH characteristics of the intestinal environment.
Cinnarizine is a vasodilator drug originally developed in Japan.
It is a very insoluble drug, intrinsic solubility 15 ng/ml,
with two basic groups with pKa values of 1.94 and 7.47, respectively.
Consequently, it is very soluble in acidic solutions. In the clinic,
the absorption of cinnarizine was found to depend on the gastric
acidity of patients. In individuals with high gastric acid content,
cinnarizine rapidly dissolves giving good absorption characteristics.
In those individuals showing low-gastric acid content, Cmax
and AUC were reduced by approximately 75 to 85%35.

Physiological factors affecting drug absorption

A more complex environmental phenomenon potentially impacting drug
absorption arises from the presence or absence of food in the gastrointestinal
tract. The presence of food can increase, decrease, or have no effect
on absorption, depending upon the characteristics of the drug and
the food. Oltipraz, for example, is a highly lipid-soluble anti-ischistosomal
agent that is practically insoluble in aqueous medium. Administration
of 500-mg tablets to fasting humans resulted in barely detectable
blood levels36. When administered in conjunction with
either a low- or high-fat meal, the drug was rapidly absorbed and
reached very high blood levels. The mechanism underlying this significantly
increased absorption is not known but is speculated to arise from
increased solubilization of the drug by bile acids secreted in response
to the meal or effects on stomach emptying. Similar observations
were also seen with  griseofulvin, hydralazine, and felodipine37.

Drugs that exhibit decreased or delayed absorption when administered with food include alendronate, furosemide, ketoprofen, theophylline, and many others. Potential mechanisms contributing to these effects are thought to include possible sequestration of free drug with food components or bile acids, thus reducing the free concentration available for absorption37. Food increases gastric emptying, which could help to improve absorption, but it also decreases small intestinal transit time that will decrease absorption for incompletely or poorly absorbed drugs.

Another potential consequence where in an increased transit has been proposed for digoxin and possibly other drugs which are substrates for P-glycoprotein in the small intestine 38. Since intestinal permeability of such compounds may depend upon the relative activity of the P-glycoprotein in the intestine, factors affecting this activity may also affect absorption. One determinant is drug concentration, which will influence the degree of saturation of the transporter. Another consideration is the specific activity of the transporter within the intestine itself. Evidence suggests that P-glycoprotein is not homogenously distributed throughout the intestinal tract but rather increases in abundance from proximal to distal small intestine 39. Therefore, drugs that may be substrates for P-gp but are fairly permeable may be well absorbed in the duodenum and proximal jejunum, which has little P-glycoprotein. If absorption is shifted to more distal parts of the small intestine by decreased transit time, however, P-glycoprotein may play a more significant role in absorption. Although definitive proof for such a mechanism has not been presented yet, it seems attractive and would contribute to the concept of an "absorption window" favoring the proximal small intestine for certain drugs which are substrates for P-gp. On the other hand, for drugs that are primarily metabolized by CYP3A4, more rapid transit would move the drug into the distal small intestine where metabolic activity is decreased 40, potentially resulting in more intact drug absorbed than for the proximal situation.

Another important confounding issue, relating absorption to structure of a molecule  is the possibility of saturable absorption or deviation from dose proportionality, but not caused by metabolism processes. Figure 5 shows  blood levels achieved as a function of dose for three clinical candidates from the oxazolidinone antibiotic class. Compound I, Zyvox, was eventually approved for treatment of gram-positive infections. It is a relatively more soluble (3.2 mg/ml) highly permeable (Pe = 24 × 10 6 cm/s in rat ileum) drug and consequently shows good absorption and dose proportionality. Compound II (Eperezolid) is similarly highly soluble (4.2 mg/ml) but has much lower permeability (Pe = 6.2 × 10 6 cm/s in rat ileum) and also exhibits a linear dose-absorption response. Compound III, however, is much less soluble (0.4 mg/ml) as well as poorly permeable (Pe = 6.6 × 10 6 cm/s in rat ileum). This situation shows a significant non-proportionality in absorption and decreasing fraction absorbed with an increase in the dose administered. 41


Fig. 5.   Dose-dependent oral absorption
of oxazolidinones in clinical studies. Linezolid (compound I) has characteristics
of high aqueous solubility and high permeability in both Caco-2 cell and perfused
rat intestine and shows good proportionality as the dose is increased. Eperezolid
(compound II) has similarly high solubility but significantly lower permeability
than compound I. 

It shows good absorption and linearity over the dosing range studied. In contrast,
compound III is a drug with both low aqueous solubility and low permeability,
resulting in poor absorption and substantially decreased fraction absorbed as
the dose is escalated.

{mospagebreak title=Variabilities in absorption literature }

Variabilities in absorption literature

Derivation of structure based models uses a set of data, permeability, and fraction absorbed, etc., which is incorporated to develop an empirical correlation with some descriptor-based representation of the drugs. The resulting model is then validated against a collection of data. The model is assumed to be predictive if the validation is successful. Since human data are derived for drugs, which in general are well behaved reasonably in vivo, the models are biased toward such compounds. For example, human permeability measurements are a fairly recent development, with the capability for such determinations. A report shows of  20 to 30 drugs that have been measured, approximately 65% are high permeability 42. High-permeability drugs are absorbed to the extent of 90% or greater in human 43. Clearly, these results in pretty sparse coverage of more poorly absorbable drugs, and hence, prediction for these compounds could be suspected.

Another consideration would be the error associated in modeling of permeability measurements, which in turn limits  conduction the experiments and the inherent variability. Naproxen, for example, has a measured permeability of 8.4 ± 3.3 × 10 4 cm/s, whereas piroxicam is reported to be 7.8 ± 7.5 × 10 4 cm/s 42. It is possible to develop highly correlative models using the mean permeability values. Given the variability of the data, an ability to confidently discriminate between even moderately dissimilar compounds may be difficult.

Similarly the observations also holds true towards absorption data. Literature in general reports  bioavailability. Where as bioavailability is a function of absorption and metabolism, however, attempts aimed mainly to  calculate fraction dose absorbed as the dependent parameter as this property is presumed to be more directly related to compound chemical and structural characteristics. In a recent and fairly comprehensive review of such literature, data for 241 drugs were described 44. Criteria were presented for estimating fraction absorbed from the bioavailability, which were then used to support the estimations. These estimations are probably reasonable, again given the diversity of the data and methodology used to quantify the in vivo results. Nevertheless, of the 241 drugs, 184 or 76% were estimated to be absorbed to the extent of 50% or greater. Of these, 108 (45% of the total) are greater than 90% absorbed. Clearly, these data are highly skewed toward well absorbed drugs, as is to be expected. From the perspective of being representative of structural and/or property diversity, however, these compounds are unlikely to adequately represent diversity of medically relevant chemistry space.

In a recent article, 44 discussed variability in bioavailability
values for a group of 282 compounds taken from literature reports
and FDA files. Although the mean experimental variability in %F
was 12, this was not uniformly distributed since the error increased
with increasing %F. Expressed as a coefficient of variation, more
highly bioavailable drugs have smaller coefficient of variation,
and this increases significantly as bioavailability decreases 45.


Absorption analysis can separate the absorption process of a drug from its disposition as such drug absorption is a very complex process that manifests itself through potential interaction with a host of physicochemical and physiological variables and also been related to drug structure, in a fairly complex ways. Complexity involved in development of predictive models can involve usage of a questionable data that affects the confidence limits of predictions (ambiguity involved in expressing the data). Secondly, even if the approximation expressed is accepted, will that best fit for  profound influences of formulation optimization on performance still remains questionable. Rather, an alternate would be to design more structure-based models for the properties contributing to the absorption process, such as solubility and permeability. These can then be used to identify opportunities for lead identification and optimization.  Thus the data obtained from the same can serve as screen to identify the leads, which can be explained as in case of a potential drug, is expected to have poor absorption as a function of low-intrinsic aqueous solubility, which becomes a  property amenable for manipulation by the formulation scientist. On the other hand, if the compound is both poorly permeable and soluble, along with a significant metabolic liability, optimization may be difficult if not impossible. Such candidates present high risks to successful development and should be identified as such early in the discovery/pre-clinical development process. Judicious development and use of computational models will clearly aid in these processes and provides an early knowledge about the probable behavior of the drug molecules and also serves as a rapid screen to for identification of lead compounds.


Abbreviations used: MAD, maximum absorbable dose; P-gp, P-glycoprotein;
SAPP, sodium acid pyrophosphate; TDRM, Total drug-related material;

1. Wessel MD, Jurs PC, Tolan JW and Muskal SM (1998) Prediction of human intestinal
absorption of drug compounds from molecular structure. J Chem Inf Comp Sci
 38: 726-735

2.Clark DE (1999) Rapid calculation of polar molecular surface area and its
application to the prediction of transport phenomena. 1. Prediction of
intestinal absorption. J Pharm Sci  88: 807-814

3.Ghuloum AM, Sage CR and Jain AN (1999) Molecular hashkeys: a novel method
for molecular characterization and its application for predicting important
pharmaceutical properties of molecules. J Med Chem  42: 1739-1748

4.Egan WJ, Merz KM, Jr and Baldwin JJ (2000) Prediciton of drug absorption
using multivariate statistics. J Med Chem  43: 3867-3877

5.Norinder U and Österberg T (2001) Theoretical calculation and prediction
of drug transport processes using simple parameters and partial least squares
projections to latent structures (PLS) statistics. The use of electrotopological
state indices. J Pharm Sci  90: 1076-1085

6.Stenberg P, Norinder U, Luthman K and Artursson P (2001) Experimental and
computational screening models for the prediction of intestinal drug absorption.
J Med Chem  44: 1927-1937

7.Zhao YH, Le J, Abraham MH, Hersey A, Eddershaw PJ, Luscombe CN, Boutina D,
Beck G, Sherborne B, Cooper I and Platts JA (2001) Evaluation of human intestinal
absorption data and subsequent derivation of a quantitative structure-activity
relationship (QSAR) with the Abraham descriptors. J Pharm Sci  90:

8.Pond SM and Tozer TN (1985) First-pass elimination. Basic concepts and clinical
consequences. Clin Pharmacokinet  9: 1-25.

9Wacher VJ, Sulphati L and Benet LZ (2001) Active secretion and enterocytic
drug metabolism barriers to drug absorption. Adv Drug Delivery Rev  46:

10. Ho NFH, Park JY, Morozowich W and Higuchi WI (1977)
Physical model approach to the design of drugs with improved intestinal absorption,
in Design of Biopharmaceutical Properties Through Prodrugs and Analogs
(Roche EB ed) pp 136-227, American Pharmaceutical Association, Academy of Pharmaceutical
Sciences, Washington, DC.

11. Sinko PJ, Leesman GD and Amidon GL (1991) Predicting fraction dose absorbed in human using a macroscopic mass balance approach. Pharm Res (NY)  8: 979-988

12. Stewart BH, Wang Y and Surendran N (2000) Ex vivo approaches to predicting oral pharmacokinetics in humans. Ann Reports in Med Chem  35: 299-307.

13. White RE (2000) High-throughput screening in drug metabolism and pharmacokinetic support of drug discovery. Ann Rev Pharmacol Toxicol  40: 133-157

14. Artursson P and Karlsson J (1991) Correlation between oral drug absorption in humans and apparent drug permeability coefficients in human intestinal epithelial (Caco-2) cells. Biochem Biophys Res Commun  14: 880-885.

15. Hilgers AR, Smith DP, Biermacher JJ, Day JS, Jensen JL, Sims SM, Adams WJ, Friis JM, Palandra J, Hosley JD, and Burton PS (2002) Predicting oral absorption of drugs: a case study with a novel class of antimicrobial agents. Pharm Res (NY), in press.

16. Johnson KC and Swindell AC (1996) Guidance in the setting of drug particle size specification to minimize variability in absorption. Pharm Res (NY)  13: 1795-1798

17. Diamond JM (1977) The epithelial junction: Bridge, gate and fence. Physiologist  20: 10-18

18. Gumbiner B (1987) Structure, biochemistry and assembly of epithelial tight junctions. Am J Physiol  253: C749-C758

19. Jackson MJ (1987) Drug transport across gastrointestinal epithelia, in Physiology of the Gastrointestinal Tract, Second Edition (Johnson LR ed) pp 1597-1621, Raven Press, New York.

20. Kottra G and Frömter E (1983) Functional properties of the paracellular pathway in some leaky epithelia. J Exp Biol  106: 217-229

21. Adson A, Burton PS, Raub TJ, Barsuhn CL, Audus KL and Ho NFH (1995) Passive diffusion of weak organic electrolytes across Caco-2 cell monolayers: uncoupling the contributions of hydrodynamic, transcellular and paracellular barriers. J Pharm Sci  84: 1197-1204

22. Tsuji A and Tamai I (1996) Carrier-mediated intestinal transport of drugs. Pharm Res (NY)  13: 963-977

23. Conradi RA, Burton PS and Borchardt RT (1996) Physico-chemical and biological factors that influence a drug's cellular permeability by passive diffusion, in Lipophilicity in Drug Action and Toxicology (Pliska V, Testa B andvan de Waterbeemd H eds) pp 233-252, VCH, Weinheim.

24. Camenisch G, Folkers G and van de Waterbeemd H (1996) Review of theoretical passive drug absorption models: historical background, recent developments and limitations. Pharm Acta Helv  71: 309-327

25. Westergaard H and Dietschy JM (1974) Delineation of the dimensions and permeability characteristics of the two major diffusion barriers to passive mucosal uptake in the rabbit intestine. J Clin Investig  54: 718-732

26. Swaan PW, Stenhouwer MC and Tukker JJ (1995) Molecular mechanism for the relative binding affinity to the intestinal peptide carrier. Comparison of three ACE-inhibitors: enalapril, enalaprilat and lisinopril. Biochim Biophys Acta  1236: 31-38

27. Li LLY, Amidon GL, Kim JS, Heimbach T, Kesisoglou F, Topliss JT and Fleisher D (2002) Intestinal metabolism promotes regional differences in apical upake of indinivar: couples effect of P-glycoprotein and cytochrome P450 3A on indinivar membrane permeability in rat. J Pharmacol Exp Ther  301: 586-593

28. Sandstrom R, Karlsson A, Knutson L and Lennernas H (1998) Jejunal absorption and metabolism of R/S-verapamil in humans. Pharm Res (NY)  15: 856-862

29. Matheny CJ, Lamb MW, Brouwer KLR and Pollack GM (2001) Pharmacokinetic and pharmacodynamic implications of P-glycoprotein modulation. Pharmacotherapy  21: 778-796

30. Stouch TR and Gudmundsson O (2002) Progress in understanding the structure-activity relationships of P-glycoprotein. Adv Drug Delivery Rev  54: 315-328

31. Seelig A (1998) A general pattern for substrate recognition by P-glycoprotein. Eur J Biochem  251: 252-261

32. Hörter D and Dressman JB (2001) Influence of physicochemical properties on dissolution of drugs in the gastrointestinal tract. Adv Drug Delivery Rev  46: 75-87

33. Wald RJ, Zipp GL, Shiou L, Connell LL, Martino AC, Stystma JA, Ruwart MJ, Rush BD, Wilkinson KF, Zhong WZ and Williams MG (1995) Non-crystallinity, supersaturation and relative bioavailability: Experiences with a non-peptidic HIV-protease inhibitor. Pharm Res (NY)  12: S6090

34. Liversidge GC and Cundy KC (1995) Particle size reduction for improvement of oral bioavailability of hydrophobic drugs: I. Absolute oral bioavailability of nanocrystalline danazol in beagle dogs. Int J Pharm  125: 91-97

35. Ogata H, Aoyagi N, Kaniwa N, Ejima A, Sekine N, Kitamura M and Inoue Y (1986) Gastric acidity dependent bioavailability of cinnarizine from two commercial capsules in healthy volunteers. Int J Pharm  29: 113-120

36. Ali HM, Homeida MMA, Sulaiman SM and Bennett JL (1984) Diet-controlled blood levels of oltipraz in healthy male subjects. J Antimicrob Chemo  13: 465-470

37. Fleisher D, Li C, Zhou Y, Pao L-H and Karim A (1999) Drug, meal and formulation interactions influencing drug absorption after oral administration. Clin Pharmacokinet  36: 233-254

38. Siegmund W (2001) Reply to the letter by Chiou WL et al.: an alternative hypothesis to involvement of intestinal P-glycoprotein as the cause for digoxin oral bioavailability enhancement by talinolol. Clin Pharmacol Therap  69: 80-81

39. Fricker G, Drewe J, Huwyler J, Gutmann H and Beglinger C (1996) Relevance of P-glycoprotein for the enteral absorption of cyclosporin A: in vitro-in vivo correlation. Brit J Pharmacol  118: 1841-1847

40. Paine MF, Khalighi M, Fisher JM, Shen DD, Kunze KL, Marsh CL, Perkins JD and Thummel KE (1997) Characterization of interintestinal and intraintestinal variations in human CYP3A-dependent metabolism. J Pharmacol Exp Therap  283: 1552-1562

41. Philip S, Burton, Jay TG, Thomas JV and Benny MA (2002) Predicting
drug absorption: how nature made it a difficult problem. J Pharmacol Exp
   303: 889-895.

42. Winiwarter S, Bonham NM, Ax F, Hallberg A, Lennernas H and Karlen A (1998) Correlation of human jejunal permeability (in vivo) of drugs with experimentally and theoretically derived parameters. A multivariate data analysis approach. J Med Chem  41: 4939-4949

43. Amidon GL, Lennernas H, Shah VP and Crison JR (1995) A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability. Pharm Res (NY)  12: 413-420

44. Andrews CW, Bennett L and Yu LX (2000) Predicting human oral bioavailability of a compound: development of a novel quantitative structure-bioavailability relationship. Pharm Res (NY)  17: 639-644

45. Yu LX, Amidon GL, Polli JE, Zhao H, Mehta MU, Conner DP, Shah VP, Lesko
LJ, Chen ML, Lee VHL, and Hussain AS (2002) Biopharmaceutics Classification
System: the scientific basis for Biowaiver extensions. Pharm Res (NY),
in press.

Hemant P. Joshi

Hemant P. Joshi

Hemanth is currently persuing his Ph.D from Rajiv Gandhi University of Health
Sciences,Bangalore, Karnataka.

Contact Info:

Hemant P. Joshi

M.Pharm, (Ph.D.)

17/B, Mahal Industrial Estate

Mahakali Caves Road, Andheri (East)

Mumbai – 400 093, INDIA


Volumes and Issues:  Reviews: