Eleven sulfur-containing conjugates were determined in the bile of rats administered with m-DCB. Mercapturic acid conjugate concentration in urine after administration of m-DCB to bile duct-cannulated rats was measured.
1. Follow ing administration of m-DCB to rats, the main metabolites in the bile were glutathione conjugates. The amount of H₂·OH-2, 4 and H₂·OH-3, 5-DCPGs accounted for about 35% of the administered dose that corresponded to 2/3 of the biliary metabolites. The biliary excretions of H₂·OH-3, 5-DCPC, 3, 5-DCPGs, H₂·OH-3, 5-DCPCG, 2, 4-DCPGs, MCPG, 3, 5-and 2, 4-DCPCs, and 2, 4-and 3, 5-DCPMs, within 4 days after administration, accounted for 5%, 4%, 3.5%, 3%, 2%, 2%, 1%, 0.1% and 0.04% of the administered dose, respectively. The sum of sulfur-containing metabolites in the bile accounted for 56% of the dose.
2. The amount (about 34% of the dose) of two mercapturic acid conjugates excreted to urine over 48-hr period after the administration of m-DCB, markedly decreased to 1/4 (about 8% of the dose) in the bile duct cannulated rats.
3. The metabolic pathway of sulfur-containing metabolites of m-DCB in rats was postulated.

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Clinical pharmacokinetic studies are conducted to evaluate the absorption, distribution, metabolism and excretion of drugs in healthy volunteers and/or patients. Data obtained from such studies are not only useful for designing and conducting subsequent clinical trials, but also, provide a scientific basis for optimizing drug therapy. Therefore, approved drugs cannot be used appropriately without the types of information obtained from clinical pharmacokinetic studies.
The Japanese Guidance on Clinical Pharmacokinetic Studies indicates the scope and basic principles to be used in designing those clinical pharmacokinetic studies required as part of the Japanese New Drug Applications (NDA) and to satisfy the requirements of Japan's post-marketing surveillance practice. The guidance also recognizes that, because each drug has its own unique set of physicochemical and pharmacokinetic properties, pharmacologic actions, toxicities, and clinical uses, exceptions to the general principles outlined in the document may occur. In the end, the goal is to design the most appropriate development process for a given investigational drug.
This article gives a brief overview of the Japanese Guidance on Clinical Pharmacokinetic Studies (the final draft), from the viewpoint of the Japanese NDA review. A brief introduction to how the ADME part of the NDA is reviewed in the Pharmaceuticals and Medical Devices Evaluation Center is also included.

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The draft of Guidance for Clinical Pharmacokinetic Studies, to be issued by Japanese health authority, has been reviewed from the view point of development at pharmaceutical industries. In general, all instructions and suggestions in the draft guidance meet with current needs, or common recognition, among scientists engaged in pharmaceutical development. The draft guidance recommends, e.g., the use of nonclinical data for logical, scientific interpretation of clinical data, as well as a number of recently acknowledged technologies in pharmaceutical science, such as pharmacogenomics, population pharmacokinetics and pharmacokinetics/pharmacodynamics. In addition, it also refers the pharmacokinetic studies in Japanese subjects conducted out of Japan, appreciating the ICH-E5 statement regarding the ethnic insensitivity of pharmacokinetic data in general. These topics and recommendations, however, seem to need further clarification and rationalization to foster industries' compliance and to increase their benefit. Namely, the criteria could have been extended and detailed for genotyping, pharmacokinetic study with intravenous dose for oral drug, the highest dose selection, linearity of pharmacokinetics, and some other specific topics.

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Guidance on drug interaction studies was prepared by the research group (Head: Professor R. Kato) and the final draft with question and answer was submitted to Ministry of Health, Labor, and Welfare on March 2001. The contents were introduced and compared with the existing CPMP and FDA guidelines on drug interaction.

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It is important for the health care professionals, such as physicians and pharmacists, to get the sufficient drug interaction data concerning the prescribed drugs. The mechanisms of the interactions were categorized generally as being pharmacokinetic or pharmacodynamic types. The guidance on drug interaction studies (draft), presented in April 2000, mainly refers to pharmacokinetic interaction rather than pharmacodynamic interaction. On the other hand, although the most basic tool for getting information on drugs is package inserts, many health care professionals has lack information on drug interaction from it. This paper discussed the guidance on drug interaction studies from the standpoint of clinical field and science, and emphasized the importance of PK/PD analysis to predict serious drug interaction for rational clinical pharmacotherapy.

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The mechanisms of drug interactions are divided into pharmacokinetic interactions and other interactions (pharmacodynamics etc). The Japanese Guidance on Drug Interaction Studies (draft), which was presented in April 2000 mainly refers to pharmacokinetic interactions and extensively covers all stages of drug development, including non-clinical, clinical, and post-marketing studies. On the other hand, drug interaction guidances have already been introduced in other regions in the world, and there are significant differences between Japan and other regions. Therefore, there is a need for worldwide harmonization (such as having it adopted as a topic of the ICH). Taking the above background into account, the Pre-Clinical Evaluation Subcommittee of the Drug Evaluation Committee of the JPMA (Japan Pharmaceutical Manufacturers Association) established a working group in the fourth division (responsible for ADME) to coordinate opinions of the pharmaceutical companies in cooperation with Clinical Evaluation Subcommittee. At the same time, we compared the Japanese draft guidance with the guidances of other regions and have already submitted the results to the regulatory authorities through the Japan Federation of Pharmaceutical Organizations (NICHIYAKUREN). This article discusses the issues that may be encountered in the actual process of interaction evaluation and differences from other regions impacting international harmonization.

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Appropriate information on drug interactions is essential for the medical practitioner when attempting to reduce the number of serious adverse events and to promote the proper use of drugs. Drug interactions should be considered from the perspective both of drugs which cause interactions (interacting drug) and drugs for which effects are affected by the interaction (interacted drug). For interacting drug, it is necessary to evaluate in vivio inhibition potential for each cytochrome P450 isozyme, such as fluvoxamine, a selective serotonin reuptake inhibitor, produce an increase in AUC values of > 150% for CYP1A2 substrate. For interacted drug, it is desirable to estimate the fraction of total plasma clearance for each cytochrome P450 isozyme. In the case of zolpidem, the incomplete dependence of its clearance on CYP3A4 activity has clinical implications for susceptibility to metabolic inhibition. These information for interacting and interacted drugs is useful for concrete management, such as changing the dose and timing of administration.

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In clinical pharmacological studies, the test drug is considered to show linear pharmacokinetics when AUC and C_max increase proportionally to dose level. In this paper, we reviewed three statistical analysis methods such as linear regression analysis, one-way analysis of variance, and a power model, which have been used to assess the dose proportionality. Power model is a simple regression analysis of log of the pharmacokinetic parameter and log of the dose. We also assessed the validity of these methods by means of computer simulation, and confirmed the usefulness of the power model. We therefore recommend the use of the power model, and reporting both the point estimate and its confidence interval of the slope.

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Statistical analysis method on nonclinical pharmacokinetic data was studied. In this paper, the AUC estimation with its variance for sparse sampling data was considered. The methods to evaluate the variance of AUC based on the mean concentration and also to estimate the approximated confidence interval were presented.

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We have studied intestinal metabolism and transport, which is considered to be sequential (1) or competitive (2) process in absorption (Scheme 1). (1) Disaccharide (maltose, cellobiose, lactose) conjugates of p-nitrophenol were hydrolyzed to p-nitrophenyl β-glucosides (p-NPβglc) on the mucosal side. p-NPβglc was transported by Na⁺/glucose cotransporter (SGLT1). Transport clearance of p-NPβglc formed from cellobiose and lactose conjugates of p-NP were higher than that from maltose or of p-NPβglc itself. These results suggest that SGLT1 is cooperatively coupled with lactase/phloridzin hydrolase catalyzing hydrolysis of cellobiose and lactose conjugates. There might be cooperative relationship between peptidase and H⁺/oligopeptide cotransporter or amino acid transporter as well. (2) Kyotorphin (KTP) was too unstable in intestine to be absorbed. KTP appeared on the serosal side in the presence of peptidase inhibitors. Meanwhile, cyclic KTP was stable in intestine to be absorbed. Absorption clearance of cyclic KTP was higher than the overall transport clearance of KTP, which was calculated according to the metabolic inhibition model. Competitive process was observed in intestinal absorption of α-naphthol as well. These results indicate that metabolism degradation and membrane transport are competitive. Unless drug is stabilized against metabolic enzyme, intestinal absorption of drug can not be improved even if membrane transport is increased.

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Human serum albumin (HSA), one of the most abundant proteins in the circulation system, contains binding sites for a variety of drugs. The recent X-ray crystallographic data of HSA shows that the two major ligand binding sites, site I and site II, are located within specialized cavities in two separate subdomains, namely subdomain IIA and IIIA, respectively. Correctly folded single-residue mutants K199A, W214A, R218H and H242Q were overexpressed. The primary binding constant for warfarin was decreased 2- and 3-fold by mutating Trp-214 and Arg-218, respectively, but was increased 4 and 6-fold by replacing Lys-199 and His-242, respectively. The constants for W214A and R218H responded to the pH-changes in a quantitatively different manner from that of K199A and H242Q. Thus, this parameter also showed heterogeneity in domain II in responding to the N to B transition. The single-mutants R410A, Y411A, Y411S and Y411F and the double-mutant R410A/Y411A were produced. We investigated, by ultrafiltration and CD, the high affinity binding of two representative site II-ligands, namely ketoprofen and diazepam. According to the crystal structure of HSA, the Arg-410 and Tyr-411 residues protrude into the center of site II (in subdomain IIIA), and the binding results showed that the guanidino moiety of Arg-410, the phenolic oxygen and the aromatic ring of Tyr-411 are important for ketoprofen binding. The guanidino moiety probably interacts electrostatically with the carboxyl group of ketoprofen, the phenolic oxygen could participate in hydrogen bonding to the ketogroup of the ligand, and the aromatic ring may participate in a specific stacking interaction with one or both of the aromatic rings of ketoprofen. In contrast, Arg-410 is not important for diazepam binding. The two parts of Tyr-411 interact favourably with diazepam and probably do so in principally the same way as with ketoprofen. In addition to its unique ligand binding properties, HSA also possesses an esterase-like activity, and studies with p-nitrophenyl acetate as a substrate showed that although Arg-410 is important, the enzymatic activity of albumin is much more dependent on the presence of Tyr-411. Minor activity was detected when serine was introduced but not alanine or phenylalanine, in position 411.

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