Medicinal Chemistry

0 downloads 19 Views 2MB Size Report
Medicinal. Chemistry. Perspective part of. How to report and discuss ADME data in medicinal chemistry .... (Figure 1) which besides CLmicr takes into account the effect of plasma ..... Broad book about in vitro–in vivo extrapolation with focus on ...



For reprint orders, please contact [email protected]

Medicinal Chemistry

How to report and discuss ADME data in medicinal chemistry publications: in vitro data or in vivo extrapolations?

Early drug discovery projects often utilize data from ADME (absorption, distribution, metabolism, elimination) assays to benchmark and guide discussion, rather than the predicted in vivo consequences of these data. Here, the two paradigms are compared, using evaluations of metabolic stability based on either microsomal clearance assay data or from the predicted in vivo hepatic clearance and half-life calculated through the combination of the venous well-stirred model and Øie-Tozer’s model. The need for a shift in paradigm is presented, and its implications discussed. It is suggested that discussions about ADME data should revolve around potential clinical problems that are most likely to surface during the development phase, each benchmarked with a suitable variable derived from the assay data.

In the drug development process, differences are often observed between in vitro and in vivo, between preclinical and clinical, between discovery and development or in less conceptual terms: between the small and the big. Bridging the gap between these different perspectives is of the highest importance in the development of an optimal discovery process. The last few decades have seen the development of rich methodologies for the determination of in vivo drug properties from in vitro assay data [1] . However, ADME (absorption, distribution, metabolism, elimination) data presented in medicinal chemistry literature and reports are usually accompanied by a discussion solely of the in vitro data, rather than predicted in vivo consequences, even though in some cases methods for in vitro to in vivo extrapolation may be used, they may not be reported (for a few exceptions [2–4]). In the same vein, go/no-go decision gates in drug discovery applications and reports in particular from academic institutions and spin-off enterprises commonly focus on assay data endpoints. With the downsizing of the drug discovery industry, academic research is believed to become increasingly important for drug discovery, necessitating a close interdisciplinary integration [5,6] . This

10.4155/FMC.14.165 © 2015 Future Science Ltd

Andreas M Svennebring Department of Pharmaceutical Biosciences, Faculty of Pharmacy, Uppsala University, Box 591, 751 24 UPPSALA, Sweden [email protected] 

article has been written to advocate a shift in focus toward evaluating and reporting in vivo extrapolations from in vitro data in medicinal chemistry journals. It is written in a simplistic manner to assure that it is comprehensible to a wide audience. For a drug to receive approval by regulatory authorities it must meet to a number of criteria [7] . These criteria are foremost concerned with the clinical properties of the prospective drug, suggesting that the criteria according to which the evaluation of data is done should preferably reflect the likely clinical properties. Here, the difference between the evaluation of ADME assay data and the use of in vitro to in vivo extrapolation is compared using the combination of ØieTozer’s model and the venous well-stirred model for the extrapolation of hepatic clearance (CLH ), volume of distribution (Vd ) and in vivo systemic half-life (t½). It is also argued that discussions should revolve around each individual potential problem that may disqualify the compounds from use as drugs, using appropriate measures of each individual potential problem: either assay data or compound variables derived from such. While this article uses as an example the role of microsomal clearance (CLmicr ), CLH

Future Med. Chem. (2015) 7(3), 259–267

part of

ISSN 1756-8919


Perspective  Svennebring

Key terms Go/no-go decision gates: Defined threshold values that primary or secondary assay data must be higher or lower than to proceed to later development stages. Clearance: Measure of drug degradation ratio as volume of fluid cleared from drug per time unit in vitro or in vivo. Microsomes: Aggregates containing metabolic enzyme formed from the endoplasmic reticulum when cell organelles are separated which are used in drug metabolism studies.

and t½ to demonstrate the difference between the use of primary and in vivo extrapolation data, the similar arguments apply also to the numerous other requirements that drugs need to obey including gut-wall absorption [8] , renal elimination [9] , drug interactions [10] , various toxicity liabilities [11,12] and for CNS active drugs the issue of blood–brain barrier penetrance [13] . CLmicr & its use in in vitro to in vivo extrapolation methods The stability of a compound toward enzymatic degradation is determined in a standardized assay in which the compound is exposed to hepatic microsomes isolated from human subjects, together with cofactors under controlled physiological conditions (pH 7.4 and 37°C). The decay of the test compound is measured and the rate of metabolism is reported as CLmicr, usually stated in μl/min/mg enzyme [14,15] . A plethora of methods for the determination of the CLH, Vd and t½ from in vitro assay data have been reported. One simple suggestion based on the venous well-stirred model [16–19] and Øie-Tozer’s model [20,21] is depicted in Figure 1. In this method, three values determined in assays are used: The CLmicr ; the free fraction (fu, i.e., 100% plasma protein binding); and the free fraction in tissue (fuT ) denoting the percentage of the drug in the tissue that exists in free form. The fuT is usually determined by measurement of the binding strength to phospholipid membranes, tissue homogenates or calculated from measured physiochemical properties [22–24] . It is an underlying assumption of the method that extrahepatic metabolism and elimination of the parent drug by the renal system and hepatic drug pumps is negligible. In vitro-in vivo extrapolation models can be built with different complexity and based on different amount of assays employed. The well-stirred model as presented here is often further built by introducing data for the extent of nonspecific binding to microsomes and the blood to plasma concentration ratio [25,26] . However, methods like the one presented can provide a rough picture of the clinically relevant properties the drug is likely to possess.


Future Med. Chem. (2015) 7(3)

CLmicr & its importance in in vivo drug properties The CLmicr is frequently used as a measure of the propensity for liabilities related to reactive metabolites. Since degraded drug must be replaced by new throughout the treatment in order to maintain an effective dose, a high CLmicr implies that a higher dose is needed, which is readily associated with a high occurrence of toxicity [27–30] . It may seem like plasma protein and tissue binding protect against degradation, and thus CLH or t½ should be a better measure. However, since only free drug is capable of affecting the drug target (the free drug hypothesis [31–33]), a high degree of binding means that the fraction of the total dose that exists in free form in vivo (the free dose fraction [34]) is low, further increasing the necessity for a higher dose and possibly offsetting the protective effect on the drug conferred by high-binding to protein. Efforts are therefore made in drug discovery programs to reduce the CLmicr. The CLmicr together with other factors determine the CLH, the effective clearance of the entire liver from a macroscopic perspective. This value is often estimated from in vitro data through the well-stirred model (Figure 1) which besides CLmicr takes into account the effect of plasma protein binding [16–19] . It is sometimes stated as the hepatic extraction rate (EH ), the fraction of the drug passing the liver which is degraded during the passage and calculated as: EH = CLH /QH, where QH is the hepatic blood flow, approximately 1.4 l/min. The EH is foremost important in relation to oral bioavailability. For polar drugs, the gastrointestinal absorption is usually the major factor contributing to drug being lost on the passage from the gastrointestinal tract to the systemic circulation [35–38] , while for nonpolar compounds, degradation during the passage through the liver is more important [19,35,36,39,40] . In the latter case, EH is a factor that needs to be followed up. Lastly, CLmicr can be used to estimate the in vivo t½ from CLH and Vd , the latter of which is determined from the plasma protein binding and data from tissue binding experiments. This is relevant from a number of perspectives. First, a high enough t½ allows the drug to stay within a suitable plasma concentration (the therapeutic window) throughout the treatment. In the beginning of continuous drug therapy, the plasma drug concentration gradually increases with each subsequent administration until it reaches concentration maxima after each dose. With time, a steady state concentration is reached, above which no further permanent increases in concentration are seen, but the concentration stabilizes and fluctuates between a minimum before a dose is administered and a maximum shortly after each administration (Figure 2) .

future science group

Perspective  Svennebring

Concentration mg/l


Toxic effects

250 200

Therapeutic window

150 100

Insufficient effect

50 0



4 6 8 Time (days)


Figure 2. The plasma concentration versus time profile of a drug with a systemic half-life of 30 h, under a one dose per day (solid line) or two doses per day (dotted line) regimen. The therapeutic window of the drug, the concentration where an insufficient drug effect is seen and where toxic effects are seen, is separated by the two horizontal lines.

The ideal use of a compound as a drug requires that the plasma concentration of said drug be maintained within the therapeutic window of the drug, a concentration interval under which the effect is too low to uphold a therapeutic effect and over which toxic effects are seen [41] . For a drug which is active over the entire day, the t½ must be long enough for the fluctuations in concentration between the doses to be within the therapeutic window between dosing periods (e.g., once per day). By splitting up the daily dose, the ability to stay within the therapeutic window throughout the day will be improved. However, doing so often reduces patient compliance with the regimen which may jeopardize the treatment [42–44] . Second, the connection between drug concentration and therapeutic effect known as the pharmacokinetic–pharmacodynamic relationship may call for a t½ in a particular range. For some antibacterial agents,

the effect is almost entirely related to the height of the maximum concentration peak, leading to a preference for drugs with a low tissue and plasma protein binding, while for other drugs, the time under which the plasma concentration is above a minimum effective threshold concentration determines the therapeutic efficacy [45–50] . In the former case, a short t½ is desired, because the exposure after the concentration maximum adds little value but may result in side effects, while in the latter case, a t½ long enough to uphold an effective concentration for as long period as possible between the dose administrations is warranted. Why in vitro to in vivo extrapolation? As apparent from Figure 1, the minimal set of properties measured in current drug discovery assays are involved in a complex interplay in vivo that determine clinical drug characteristics such as its CLH, Vd and t½. Thus, CLmicr is a poor replacement for discussions where CLH or t½ is a more relevant parameter to follow. Calculating the CLH and t½ for a compound with a CLmicr of 20 μl/min/mg according to the methodology presented in Figure 1 for a compound with low fu and fuT (Table 1, number 1), and for one with high (number 2), demonstrates the importance of evaluating the nonspecific binding to biomolecules based on the calculated in vivo data. The value 20 μl/min/mg can indicate a CLH from liters to hundreds of liters per hour, and a t½ anywhere from below an hour to above a month. Thus, CLmicr is not a meaningful replacement in discussions where CLH or t½ is the most suitable parameter to benchmark. In primary data evaluation, CLmicr below 10  μl/min/mg is generally perceived as good, up to 50 μl/min/mg as potentially useful and above 50  μl/min/mg as unacceptable, although different opinions exist. Because numerous potential problems

Table 1. Comparison of in vitro half-life between two compounds of equal microsomal clearance but different level of free fraction and free fraction in tissue. Variable

Number 1

Number 2



























= 37


Calculated in vitro t1/2 using the model described in Figure 1 for two compounds, both with a measured CLmicr of 20 μl/min/mg protein and two alternative sets of plasma protein binding and Vd. The following constants have been used in the calculations: total liver enzyme content = 90 g, Vp = 0.0436 l/kg, VE = 0.151 l/kg, ER = 0.38 l/kg and RE/I = 1.4. QH = 84 l/h. An individual weighing 70 kg has been assumed. CLH : Hepatic clearance; CLmicr : Microsomal clearance; fuT: Free fraction in tissue; fu : Free fraction; l/h: Liters per hour; t½ : Half-life; Vd : Volume of distribution.


Future Med. Chem. (2015) 7(3)

future science group

How to report & discuss ADME data in medicinal chemistry publications: in vitro data or in vivo extrapolations 

are dependent on the CLmicr and affected by other variables in different ways, it is difficult to find a rationale behind these factors as sole criteria for judgment. A better evaluation and more relevant discussion about ADME data should revolve around each individual problem that may lead to attrition during the development rather than each assay and potential problems should be benchmarked through an appropriate parameter, primary data or derived from primary data. The CLmicr is generally accepted to be a relevant measure of potential toxicity liabilities due to reactive intermediates [27–30] . In cases where first-pass hepatic metabolism is likely to be problematic, EH is a more relevant parameter to discuss [51,52] , while t½ briefly indicates the prerequisites for upholding a stable plasma concentration without the need for the development of a more complex formulation. Setting go/no-go thresholds entirely based on CLmicr is also complicated by the difficulties in finding a rationale for the thresholds suggested. The CLmicr of most drugs spans the range of 1–1000 μl/min/mg [18] . Using a threshold of 1000 μl/min/mg to parse the compound portfolio would hardly remove any compounds, and lowering it would lead to the paradox that a considerable amount of approved drugs will fail to pass. In contrast, valuable information about what is demanded of compounds can be found in regulatory documents and clinical literature. It is generally believed that the oral bioavailability should be no lower than 30% for any drug, and in many cases, a considerably higher value is needed, in particular for drugs with a narrow therapeutic window [14,15] . This value reflects not just first-pass hepatic metabolism, but also the drug being lost due to incomplete dissolution, absorption through and degradation during the passage through the gastrointestinal wall. For a nonpolar drug, gastrointestinal absorption is usually minor in relation to first-pass metabolism and can thus be expected to make a minor contribution. However, for a polar drug, it must be considered that a substantial amount of drug will be lost prior to reaching the liver [36,53,54] . The evaluation and selection of compounds based on t½ should be done with caution. Two factors are particularly important in the determination of a suitable t½ : the therapeutic index [41] (defined as the quotient between the upper and lower limit of the therapeutic window), and the time interval between the doses. With a low therapeutic index, the t½ must be higher to allow for the concentration to stay within the therapeutic window throughout the treatment. Determination of the therapeutic index cannot be done in early drug discovery. However, a few assumptions can be made: for drugs with endogenous targets (in contrast to parasite targets), adverse effects and toxicity are often related to on- or

future science group


Key terms Primary data: In this context, data obtained from assays in contrast to data that have been obtained through calculations based on assay data. Therapeutic index: Quotient between the upper and lower limit of the therapeutic window. On-target activity: Medical effects caused by a drug due to its effect on the receptor yielding its therapeutic effect. Off-target activity: Medical effects caused by a drug due to its effect on other receptors than those yielding its therapeutic effect. Extended-release formulations: Drug formulation that is releasing drug slowly during the gastrointestinal passage to give a more stable plasma drug concentration.

off-target activities. This is more seldom the case for anti-infective drugs: while a neuronal receptor agonist may produce unwanted side effects too high of a plasma concentration due to over-activation of the system or promiscuous activity at other receptors, the problem of entirely eradicating bacteria is not a limiting consideration. Many chemotherapeutics therefore undergo several t½ between each dose administration, while drugs targeting endogenous receptors generally need a t½ that is longer than the interval between two doses. Some therapeutic areas are often associated with a narrow therapeutic index, most notably anticoagulants, antiepileptics and cardiac glycosides calling for a longer t½ [55,56] . It is possible to reduce fluctuations throughout the day by splitting doses between administrations, although this often reduces patient compliance with the drug regimen [42,44] . When the treatment is indicated for a short time, giving immediate relief to unpleasant symptoms, or used against a very severe condition, this may motivate the patient to more fully comply [57] . Too low of a t½ can often be compensated for by the use of extendedrelease formulations, though this may increase the cost and complexity of formulation [58,59] . A too high t½ may complicate the use of drugs because of the time required to reach therapeutic concentrations [60,61] . Future perspective In vitro based systems are sometimes regarded as alternatives to animal experiments and sometimes as orthologous models. The accuracy of animal pharmacokinetic investigations are generally inferior to in vitro-based methods in its ability to predict human pharmacokinetics, but the price is considerably higher for animal-based methods. These factors make in vitrobased methods suitable for the selection of compounds for animal investigations [62–66] . The use of animal experiments is subject to significant ethical regulation and requires permissions from


Perspective  Svennebring licensing and governing bodies. The trend is that the restrictions are increasing, further complicating these investigations, and higher standards for animal care may increase costs. Errors in the mathematical translation of animal data to man (allometric scaling) are largely due to differences between the species, giving little hope for improvements [67,68] . However, for in vitrobased methods, improvements are continuously made allowing for increased predictability and lower costs. Systems like the one depicted in Figure 1 have the drawback that errors from each individual method are multiplied when the methods are connected together (so called compounding of errors). Still, they are used at several large pharmaceutical companies active in drug discovery. Many times, it can be valuable just to identify whether a potential problem like first-pass hepatic metabolism is likely and should to be considered in the design of new compound series, or whether it is too minor to warrant attention (a sensitivity analysis). The choice of assays, in vitro to in vivo extrapolation methods and guidelines for the interpretation of data and for the establishment of go/no-go decision gates is a complicated matter which cannot be fully covered in this article. However, the following three steps could be a viable path in guiding decisions for academic groups with a limited budget: formulate needs and preferences in quantitative terms, and determine the most critical requirements to fulfill. Estimates of the important properties can be determined in silico. By comparing the predicted data with the thresholds established, it can be determined whether the structure most likely will fulfill the different endpoints and thus does not necessarily demand testing, whether they are likely to miss target values and should be followed up through assays and in vivo extrapolations. If the estimates indicate that the properties are too unlikely to be fulfilled, this indicates that the compounds shouldn’t be synthesized. Choose assays and in vitro to in vivo extrapolation methods based on the most critical needs with consideration of the available budget. To shift the focus from primary data to in vivo extrapolations, a number of means are available: the undergraduate education in medicinal chemistry often centers on a curriculum of pharmacokinetics. However, the literature used is usually focused on clinical pharmacokinetics while the very important link between experimental findings and clinical outcomes is lacking. For the individual interested in in vitro to in vivo extrapolation, very little literature at the undergraduate level is available. At best, reviews in peerreviewed journals covering specific areas such as liver extraction or gut-absorption models are available but require considerable knowledge in pharmacokinetics to fully understand. Drug discovery is largely driven


Future Med. Chem. (2015) 7(3)

by applications and reports, the authoring of which is regulated by instructions from funding bodies and journals. Every applicant for funding is undoubtedly sensitive to directions and recommendations which thus becomes a potential way to advocate for the use of methods for in vitro to in vivo extrapolation. Larger funding bodies such as the National Institute of Health may also provide aid toward the implementation of such routines. Medicinal chemistry journals in which most drug discovery projects are reported can give direction, and subject articles including ADME data to dedicated ADME referee review. Contract Research Organizations (CROs) performing assays for early drug discovery often offer packages with several assays. It could be of great value especially to the academic drug discovery community if the reports from such investigations also included predictions of in vivo behavior based on the models. However, such services will only be offered by CROs in response to customer demands. Conclusion Compounds in a drug discovery pipeline must be scrutinized using criteria as similar as possible to the qualities they will be measured against in the possible future clinical development phase. When interpreting and making decisions based on in vitro pharmacokinetic data, emphasis on predicted in vivo behavior will ultimately result in a better understanding of how a compound will perform in clinical applications. Results from clinical sciences can guide the selection of relevant threshold values that can be implemented for early go/no-go decisions. Discussions about ADME data should focus on the problems that are most likely to cause attrition, the extent of each should be benchmarked with a suitable parameter describing the extent of the problem. Changes in undergraduate education curriculum, funding body organization, journal directions and CRO product portfolios are proposed to facilitate this change in direction. Acknowledgement The author wishes to thank R Olsen and NM Kane for valuable linguistic suggestions.

Financial & competing interests disclosure The author has no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending or royalties. No writing assistance was utilized in the production of this manuscript.

future science group

How to report & discuss ADME data in medicinal chemistry publications: in vitro data or in vivo extrapolations 


Executive summary • In vitro to in vivo extrapolation allows for the prediction of in vivo drug properties from in vitro assay data. • In medicinal chemistry journals and reports, ADME data are usually evaluated rather than in vivo extrapolations. • From microsomal clearance (CLmicr), plasma protein binding and tissue binding assay data, hepatic clearance (CLH ) and systemic half-life (t½ ) can be extrapolated. • CLmicr, extrapolated CLH and systemic t½ are used for different purposes. • CLmicr cannot be used as a replacement for purposes where extrapolated CLH and systemic t½ is a more correct parameter. • The reporting of in vitro to in vivo extrapolation from assay data may be promoted by education, regulations and instructions from funding bodies and journals; and through changes in the product portfolio of contract research organizations.

References Papers of special note have been highlighted as: • of interest; •• of considerable interest 1

Chilukuri DM, Sunkara G, Young D. Pharmaceutical product development. In: In Vitro–In Vivo Correlation. Informa Healthcare, Zug, Switzerland (2007).


Shirude PS, Shandil RK, Manjunatha MR et al. Lead optimization of 1,4-azaindoles as antimycobacterial agents. J. Med. Chem. 57(13), 5728–5737 (2014).

In vitro–in vivo extrapolation method for determination of drug interactions.


Yoon M, Campbell JL, Andersen ME, Clewell HJ. Quantitative in vitro to in vivo extrapolation of cell-based toxicity assay results. Crit. Rev. Toxicol. 42(8), 633–652 (2012).

In vitro–in vivo extrapolation method for determination of toxicity.


Gulden M, Seibert H. In vitro–in vivo extrapolation: estimation of human serum concentrations of chemicals equivalent to cytotoxic concentrations in vitro. Toxicology 189(3), 211–222 (2003).


Broad book about in vitro–in vivo extrapolation with focus on pharmaceutics.


Hibi S, Ueno K, Nagato S et al. Discovery of 2-(2-oxo-1phenyl-5-pyridin-2-yl-1,2-dihydropyridin-3-yl)benzonitrile (perampanel): a novel, noncompetitive alpha-amino-3hydroxy-5-methyl-4-isoxazolepropanoic acid (AMPA) receptor antagonist. J. Med. Chem. 55(23), 10584–10600 (2012).


Ball K, Bouzom F, Scherrmann JM, Walther B, Decleves X. Physiologically based pharmacokinetic modelling of drug penetration across the blood-brain barrier–towards a mechanistic IVIVE-based approach. AAPS J. 15(4), 913–932 (2013).


Chen Y, Jin JY, Mukadam S, Malhi V, Kenny JR. Application of IVIVE and PBPK modeling in prospective prediction of clinical pharmacokinetics: strategy and approach during the drug discovery phase with four case studies. Biopharm. Drug Dispos. 33(2), 85–98 (2012).

In vitro–in vivo extrapolation method for determination of blood–brain barrier penetrance.


Hellriegel ET, Bjornsson TD, Hauck WW. Interpatient variability in bioavailability is related to the extent of absorption: implications for bioavailability and bioequivalence studies. Clin. Pharmacol. Ther. 60(6), 601–607 (1996).


Veber DF, Johnson SR, Cheng H-Y, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 45(12), 2615–2623 (2002).


Frearson J, Wyatt P. Drug discovery in academia- the third way? Expert Opin. Drug Discov. 5(10), 909–919 (2010).


Williams M. Productivity shortfalls in drug discovery: contributions from the preclinical sciences? J. Pharmacol. Exp. Ther. 336(1), 3–8 (2011).


Food and Drug Administration FDA Guidelines for Industry Series. Rockville, MD,USA (1999).



Cho HJ, Kim JE, Kim DD, Yoon IS. In vitro–in vivo extrapolation (IVIVE) for predicting human intestinal absorption and first-pass elimination of drugs: principles and applications. Drug Dev. Ind. Pharm. 40(8), 989–998 (2014).

Gillette JR. Factors affecting drug metabolism. Ann. NY Acad. Sci. 179, 43–66 (1971).


Chao P, Uss AS, Cheng K. Use of intrinsic clearance for prediction of human hepatic clearance. Expert Opin. Drug Metab. Toxicol. 6(2), 189–198 (2010).


Review about in vitro–in vivo extrapolation of gastrointestinal absorption and first-pass metabolism. 

Review on the venous well-stirred model and similar methods (liver extraction models).


Kunze A, Huwyler J, Poller B, Gutmann H, Camenisch G. In vitro–in vivo extrapolation method to predict human renal clearance of drugs. J. Pharm. Sci. 103(3), 994–1001 (2014).


In vitro–in vivo extrapolation method for determination of renal clearance. 

Ito K, Houston JB. Comparison of the use of liver models for predicting drug clearance using in vitro kinetic data from hepatic microsomes and isolated hepatocytes. Pharm. Res. 21(5), 785–792 (2004).



Wienkers LC, Heath TG. Predicting in vivo drug interactions from in vitro drug discovery data. Nat. Rev. Drug Discov. 4(10), 825–833 (2005).

Svennebring A. Investigation of the prerequisites for the optimization of specific plasma protein binding as a strategy for the reduction of first-pass hepatic metabolism. Xenobiotica (2014) (Epub ahead of print).

future science group


Perspective  Svennebring •

Describe how high plasma protein binding can be strategically used to protect from first-pass hepatic metabolism.


Kerns E, Di L. Drug-Like Properties: Concepts, Structure Design and Methods. Academic Press, Waltham, Massachusetts, (2008).


Waters NJ, Lombardo F. Use of the Oie-Tozer model in understanding mechanisms and determinants of drug distribution. Drug Metab. Dispos. 38(7), 1159–1165 (2010).


Svennebring A. Visualization of plasma and tissue binding using dose fractions parameter. Drug Dev. Res. 75(7), 425–437 (2014).

Demonstration of Øie-Tozer’s model.

Presentation of the dose fraction concept.


Oie S, Tozer TN. Effect of altered plasma protein binding on apparent volume of distribution. J. Pharm. Sci. 68(9), 1203–1205 (1979).



Hollosy F, Valko K, Hersey A, Nunhuck S, Keri G, Bevan C. Estimation of volume of distribution in humans from high throughput HPLC-based measurements of human serum albumin binding and immobilized artificial membrane partitioning. J. Med. Chem. 49(24), 6958–6971 (2006).

van de Waterbeemd H, Lennernäs H, Artursson P, Mannhold R, Kibinyu R, Folkers G. Drug Bioavailability: Estimation of Solubility, Permeability, Absorption and Bioavailability. Wiley-VCH, Weinheim, Germany (2003).

Simple method for determination of tissue-free fraction in vitro.


Valko K, Du CM, Bevan CD, Reynolds DP, Abraham MH. Rapid-gradient HPLC method for measuring drug interactions with immobilized artificial membrane: comparison with other lipophilicity measures. J. Pharm. Sci. 89(8), 1085–1096 (2000).


Berry LM, Roberts J, Be X, Zhao Z, Lin M-HJ. Prediction of V(ss) from in vitro tissue-binding studies. Drug Metab. Dispos. 38(1), 115–121 (2010).


Wan H, Bold P, Larsson LO et al. Impact of input parameters on the prediction of hepatic plasma clearance using the well-stirred model. Curr. Drug Metab. 11(7), 583–594 (2010).







Austin RP, Barton P, Mohmed S, Riley RJ. The binding of drugs to hepatocytes and its relationship to physicochemical properties. Drug Metab. Dispos. 33(3), 419–425 (2005). Kalgutkar AS, Gardner I, Obach RS et al. A comprehensive listing of bioactivation pathways of organic functional groups. Curr. Drug Metab. 6(3), 161–225 (2005). Uetrecht JP. New concepts in immunology relevant to idiosyncratic drug reactions: the “danger hypothesis” and innate immune system. Chem. Res. Toxicol. 12(5), 387–395 (1999). Lammert C, Einarsson S, Saha C, Niklasson A, Bjornsson E, Chalasani N. Relationship between daily dose of oral medications and idiosyncratic drug-induced liver injury: search for signals. Hepatology 47(6), 2003–2009 (2008). Stepan AF, Walker DP, Bauman J et al. Structural alert/ reactive metabolite concept as applied in medicinal chemistry to mitigate the risk of idiosyncratic drug toxicity: a perspective based on the critical examination of trends in the top 200 drugs marketed in the United States. Chem. Res. Toxicol. 24(9), 1345–1410 (2011).


Broad book on bioavailability.


Dressman JB, Thelen K, Jantratid E. Towards quantitative prediction of oral drug absorption. Clin Pharmacokinet. 47(10), 655–667 (2008).


Palm K, Luthman K, Ungell AL, Strandlund G, Artursson P. Correlation of drug absorption with molecular surface properties. J. Pharm. Sci. 85(1), 32–39 (1996).


Palm K, Stenberg P, Luthman K, Artursson P. Polar molecular surface properties predict the intestinal absorption of drugs in humans. Pharm. Res. 14(5), 568–571 (1997).


Seydel JK, Schaper KJ. Quantitative structurepharmacokinetic relationships and drug design. Pharmacol. Ther. 15(2), 131–182 (1982).


Toon S, Rowland M. Structure-pharmacokinetic relationships among the barbiturates in the rat. J. Pharmacol. Exp. Ther. 225(3), 752–763 (1983).


Muller PY, Milton MN. The determination and interpretation of the therapeutic index in drug development. Nat. Rev. Drug Discov. 11(10), 751–761 (2012).


Claxton AJ, Cramer J, Pierce C. A systematic review of the associations between dose regimens and medication compliance. Clin. Ther. 23(8), 1296–1310 (2001).


Jin J, Edward Sklar G, Min Sen Oh V, Chuen Li S. Factors affecting therapeutic compliance: a review from the patient’s perspective. Ther. Clin. Risk Manag. 4(1), 269–286 (2008).


Tashkin DP. Multiple dose regimens. Impact on compliance. Chest 107(Suppl. 5), s176–s182 (1995).


Rodvold KA. Pharmacodynamics of antiinfective therapy: taking what we know to the patient’s bedside. Pharmacotherapy 21(11 Pt 2), s319–s330 (2001).


Gunderson BW, Ross GH, Ibrahim KH, Rotschafer JC. What do we really know about antibiotic pharmacodynamics? Pharmacotherapy 21(11 Pt 2), s302–s318 (2001).


Nicolau DP. Optimizing outcomes with antimicrobial therapy through pharmacodynamic profiling. J. Infect Chemother. 9(4), 292–296 (2003).


Smith DA, Di L, Kerns EH. The effect of plasma protein binding on in vivo efficacy: misconceptions in drug discovery. Nat. Rev. Drug Discov. 9(12), 929–939 (2010).


Frimodt-Moller N. How predictive is PK/PD for antibacterial agents? Int. J. Antimicrob. Agents 19(4), 333–339 (2002).


van Steeg TJ. The ‘free drug hypothesis’: fact or fiction? In: Department Pharmacology. Leiden/Amsterdam Center for Drug Research, (LACDR), Faculty of Science. Leiden University, Leiden (2008).


Li RC, Zhu ZY. The integration of four major determinants of antibiotic action: bactericidal activity, postantibiotic effect, susceptibility, and pharmacokinetics. J. Chemother. 14(6), 579–583 (2002).

Future Med. Chem. (2015) 7(3)

future science group

How to report & discuss ADME data in medicinal chemistry publications: in vitro data or in vivo extrapolations 


Vinks A, Derendorf H, Mouton J. Fundamentals of Antimicrobial Pharmacokinetics and Pharmacodynamics. Springer eBooks, Berlin, Germany (2014).


Allam A, El gamal S, Naggar V. Bioavailability: a pharmaceutical review. J. Novel Deliv. Tech. 1, 80–96 (2011).


Thomas VH, Bhattachar S, Hitchingham L et al. The road map to oral bioavailability: an industrial perspective. Expert Opin. Drug Metab. Toxicol. 2(4), 591–608 (2006).


Wang J, Skolnik S. Permeability diagnosis model in drug discovery: a diagnostic tool to identify the most influencing properties for gastrointestinal permeability. Curr. Top Med. Chem. 13(11), 1308–1316 (2013).


Hou T, Wang J, Zhang W, Xu X. ADME evaluation in drug discovery. 7. Prediction of oral absorption by correlation and classification. J. Chem. Inf. Model 47(1), 208–218 (2007).


Burns M. Management of narrow therapeutic index drugs. J. Thromb. Thrombolysis 7(2), 137–143 (1999).


Schneiderman MA, Myers MH, Sathe YS, Koffsky P. Toxicity, the therapeutic index, and the ranking of drugs. Science 144, 1212–1213 (1964).


Jin J, Sklar GE, Min Sen Oh V, Chuen Li S. Factors affecting therapeutic compliance: a review from the patient’s perspective. Ther. Clin. Risk Manag. 4(1), 269–286 (2008).


Rajesh A, Harish R, Sangeeta A. Sustained release drug technology: a review. IJRPS 2(4), 1–13 (2012).

Review on extended release formulations.


Abhijit Ratilal D, Priti G, Vidyadhar B, Sunil P. A review on: sustained release technology. IJRAP 2(6), 1701–1708 (2011).


Wen H, Park K. Oral Controlled Release Formulation Design and Drug Delivery: Theory to Practice Wiley, Hoboken (2010).

future science group


Toutain PL, Bousquet-Melou A. Plasma terminal half-life. J. Vet. Pharmacol. Ther. 27(6), 427–439 (2004).


Poulin P, Jones HM, Jones RD et al. PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 1: goals, properties of the PhRMA dataset, and comparison with literature datasets. J. Pharm. Sci. 26(10), 22554 (2011).


Jones RD, Jones HM, Rowland M et al. PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 2: comparative assessment of prediction methods of human volume of distribution. J. Pharm. Sci. 30(10), 22553 (2011).


Ring BJ, Chien JY, Adkison KK et al. PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 3: comparative assessement of prediction methods of human clearance. J. Pharm. Sci. 3(10), 22552 (2011).


Vuppugalla R, Marathe P, He H et al. PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 4: prediction of plasma concentration-time profiles in human from in vivo preclinical data by using the Wajima approach. J. Pharm. Sci. 7(10), 22551 (2011).


Poulin P, Jones RD, Jones HM et al. PHRMA CPCDC initiative on predictive models of human pharmacokinetics, part 5: prediction of plasma concentration-time profiles in human by using the physiologically-based pharmacokinetic modeling approach. J. Pharm. Sci. 3(10), 22550 (2011).


Toutain P-L, Ferran A, Bousquet-Melou A. Species differences in pharmacokinetics and pharmacodynamics. Handb. Exp. Pharmacol. 199, 19–48 (2010).


Tang H, Mayersohn M. Controversies in allometric scaling for predicting human drug clearance: an historical problem and reflections on what works and what does not. Curr. Top Med. Chem. 11(4), 340–350 (2011).