Adverse Event Signal Detection Using Patients' Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models.

BACKGROUND: Early detection of adverse events and their management are crucial to improving anticancer treatment outcomes, and listening to patients' subjective opinions (patients' voices) can make a major contribution to improving safety management. Recent progress in deep learning technologies has enabled various new approaches for the evaluation of safety-related events based on patient-generated text data, but few studies have focused on the improvement of real-time safety monitoring for individual patients. In addition, no study has yet been performed to validate deep learning models for screening patients' narratives for clinically important adverse event signals that require medical intervention. In our previous work, novel deep learning models have been developed to detect adverse event signals for hand-foot syndrome or adverse events limiting patients' daily lives from the authored narratives of patients with cancer, aiming ultimately to use them as safety monitoring support tools for individual patients. OBJECTIVE: This study was designed to evaluate whether our deep learning models can screen clinically important adverse event signals that require intervention by health care professionals. The applicability of our deep learning models to data on patients' concerns at pharmacies was also assessed. METHODS: Pharmaceutical care records at community pharmacies were used for the evaluation of our deep learning models. The records followed the SOAP format, consisting of subjective (S), objective (O), assessment (A), and plan (P) columns. Because of the unique combination of patients' concerns in the S column and the professional records of the pharmacists, this was considered a suitable data for the present purpose. Our deep learning models were applied to the S records of patients with cancer, and the extracted adverse event signals were assessed in relation to medical actions and prescribed drugs. RESULTS: From 30,784 S records of 2479 patients with at least 1 prescription of anticancer drugs, our deep learning models extracted true adverse event signals with more than 80% accuracy for both hand-foot syndrome (n=152, 91%) and adverse events limiting patients' daily lives (n=157, 80.1%). The deep learning models were also able to screen adverse event signals that require medical intervention by health care providers. The extracted adverse event signals could reflect the side effects of anticancer drugs used by the patients based on analysis of prescribed anticancer drugs. "Pain or numbness" (n=57, 36.3%), "fever" (n=46, 29.3%), and "nausea" (n=40, 25.5%) were common symptoms out of the true adverse event signals identified by the model for adverse events limiting patients' daily lives. CONCLUSIONS: Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients' subjective information recorded in pharmaceutical care records accumulated during pharmacists' daily work.

Detection of Adverse Event Signals with Severity Grade Classification from Cancer Patient Narrative.

Adverse event (AE) management is crucial to improve anti-cancer treatment outcomes, but it is reported that some AE signals can be missed in clinical visits. Thus, monitoring AE signals seamlessly, including events outside hospitals, would be helpful for early intervention. Here we investigated how to detect AE signals from texts written by cancer patients themselves by developing deep-learning (DL) models to classify posts mentioning AEs according to severity grade, in order to focus on those that might need immediate treatment interventions. Using patient blogs written in Japanese by cancer patients as a data source, we built DL models based on three approaches, BERT, ELECTRA, and T5. Among these models, T5 showed the best F1 scores for both Grade ≥ 1 and ≥ 2 article classification tasks (0.85 and 0.53, respectively). This model might benefit patients by enabling earlier AE signal detection, thereby improving quality of life.

A Survey of the Status of Methadone Switching in Japan Using a Hospital-Based Administrative Claims Database.

Methadone is generally used for the management of cancer pain in patients who cannot obtain adequate analgesia from other strong opioids; however, it has a complicated and inconsistent conversion ratio from pre-switching opioid dosage to methadone. This issue may be pronounced in Japan because only oral tablets are commercially available. We aimed to elucidate the status of methadone switching in Japan, focusing on its dosage. Using a Japanese hospital-based administrative claims database, we included patients who switched to methadone between April 2008 and January 2021. The proportion of methadone switching completion that required more than the defined conversion ratio in the Japanese package insert (called "high-dose methadone switching") was evaluated as a primary endpoint. Other endpoints included "the duration from initiation to completion of methadone switching" and "factors affecting high-dose methadone switching by using multivariate logistic regression analysis". Of 1585 patients who received methadone, 370 were enrolled. Among those, 130 (35.1%) received high-dose methadone switching. The median duration of methadone switching completion (12 days) was longer in the high-dose methadone switching group than in other patients. Four variables were identified as factors affecting high-dose methadone switching. Younger age and outpatient status increased the risk of requiring high-dose methadone switching, whereas the concomitant use of nonsteroidal anti-inflammatory drugs and fentanyl as a pre-switching opioid decreased the risk. In conclusion, more than 30% of the patients underwent high-dose methadone switching and required long completion periods, suggesting that methadone switching remains challenging in Japan.

Comparison of different sustained-release opioids and acute respiratory conditions in patients with cancer and chronic kidney disease.

STUDY OBJECTIVE: Few data are available on the association between the use of oxycodone in patients with chronic kidney disease (CKD) and acute respiratory conditions. The aim of this study was to investigate whether oxycodone is associated with an increased risk of acute respiratory conditions in patients with cancer and CKD compared with other opioids. DESIGN AND SETTING: The data were obtained from a claims database in Japan. Patients with cancer and CKD who had received sustained-release opioids, including oral oxycodone, oral morphine, or transdermal fentanyl, between April 2014 and May 2021 were selected. The primary outcome was defined as an acute respiratory condition. Data for age and sex, morphine equivalent daily dose, concomitant use of specified medications, comorbidities defined based on the modified Charlson comorbidity index, substance use disorder, and lung cancer or metastatic lung cancer were investigated as covariates. Distribution of acute respiratory conditions was compared among the three sustained-release opioid groups using the log-rank test. Estimates of the incidence of acute respiratory conditions were compared among the groups using a Cox proportional hazards model with time-varying variables. MAIN RESULTS: A significant difference in the distribution of acute respiratory conditions was found among the three groups (p < 0.01). Cox regression analysis showed a significantly higher risk of acute respiratory conditions with morphine (hazard ratio [HR]: 3.04, 95% confidence interval [CI]: 1.07-8.65, p = 0.04) compared with oxycodone but no significant difference in risk with oxycodone (HR 0.67, 95% CI: 0.32-1.38, p = 0.27) compared with fentanyl. CONCLUSIONS: The findings suggest that the risk of acute respiratory conditions may be lower in patients with CKD who use oxycodone for cancer pain than in those who use morphine. Additionally, no difference in the risk of acute respiratory conditions was found between oxycodone and fentanyl use.

Serious Bacterial Infections Associated with Eculizumab: A Pharmacovigilance Study.

Objective Molecular-targeted agents, including eculizumab and rituximab, are considered treatment options for refractory myasthenia gravis (MG), but bacterial infections can occur as serious adverse events when using these agents. The present study elucidated the relative risks of bacterial infections associated with eculizumab and rituximab using a pharmacovigilance database. Methods We analyzed eculizumab- and rituximab-associated adverse events reported between 2007 and 2021 in the US Food and Drug Administration Adverse Event Reporting System (FAERS) and herein report a refractory MG patient who developed streptococcal toxic shock syndrome during eculizumab treatment. Patients We evaluated a 74-year-old Japanese woman with refractory MG who developed severe bacteremia after receiving eculizumab. Results A total of 44,215 and 108,485 adverse events were reported with eculizumab and rituximab, respectively, from among 13,742,321 individual case safety reports in the FAERS database after data cleaning. We found a strong association between eculizumab and Neisseria infections. In contrast, we found only one case of meningococcal meningitis treated with rituximab. Both eculizumab and rituximab were weakly associated with streptococcal infections. Two cases of streptococcal toxic shock syndrome were associated with rituximab. Conclusion Careful monitoring of serious bacterial infections associated with eculizumab treatment is warranted.

Factors associated with lenvatinib adherence in thyroid cancer and hepatocellular carcinoma.

BACKGROUND: Lenvatinib is an oral anticancer medication used to treat radioiodine-refractory thyroid cancer and unresectable hepatocellular carcinoma. The purpose of this study is to evaluate lenvatinib adherence by patients and to identify factors associated with decreased lenvatinib adherence. METHODS: Among 153 patients who started treatment with lenvatinib for unresectable thyroid cancer or unresectable hepatocellular carcinoma between May 1, 2015 and August 31 2021 at the Cancer Institute Hospital of the Japanese Foundation for Cancer Research, 102 were eligible for this study (55 thyroid cancer, 47 hepatocellular carcinoma). The lenvatinib adherence rate in a treatment cycle was defined as the number of times a patient took lenvatinib in a 28-day cycle divided by the prescribed 28 doses. The rate was determined by pill counting and self-reporting at the pharmaceutical outpatient clinic. Reasons for non-adherence were established by interview and analyzed. RESULTS: The median adherence rate of lenvatinib in the first cycle was 90.1% (n = 55) in thyroid cancer and 94.9% (n = 47) in hepatocellular carcinoma. In thyroid cancer, there were 255 incidents of lenvatinib non-adherence. Non-adherence was mainly associated with bleeding events (18.6%), followed by hand-foot skin reactions (10.6%). In hepatocellular carcinoma, there were 97 incidents of non-adherence. Hypertension accounted for 20.6%, followed by hoarseness (18.6%) and diarrhea (17.5%). CONCLUSION: The adherence rate for lenvatinib in Japanese patients with thyroid and hepatocellular carcinoma in real-world clinical practice was more than 90% in this study. Hypertension was a major reason for non-adherence, followed by hand-foot skin reactions and diarrhea.

Adverse event signal extraction from cancer patients' narratives focusing on impact on their daily-life activities.

Adverse event (AE) management is important to improve anti-cancer treatment outcomes, but it is known that some AE signals can be missed during clinical visits. In particular, AEs that affect patients' activities of daily living (ADL) need careful monitoring as they may require immediate medical intervention. This study aimed to build deep-learning (DL) models for extracting signals of AEs limiting ADL from patients' narratives. The data source was blog posts written in Japanese by breast cancer patients. After pre-processing and annotation for AE signals, three DL models (BERT, ELECTRA, and T5) were trained and tested in three different approaches for AE signal identification. The performances of the trained models were evaluated in terms of precision, recall, and F1 scores. From 2,272 blog posts, 191 and 702 articles were identified as describing AEs limiting ADL or not limiting ADL, respectively. Among tested DL modes and approaches, T5 showed the best F1 scores to identify articles with AE limiting ADL or all AE: 0.557 and 0.811, respectively. The most frequent AE signals were "pain or numbness", "fatigue" and "nausea". Our results suggest that this AE monitoring scheme focusing on patients' ADL has potential to reinforce current AE management provided by medical staff.

Dysautonomia associated with immune checkpoint inhibitors.

OBJECTIVE: The purpose of this study is to report the clinical characteristics of dysautonomia associated with immune checkpoint inhibitors (ICIs). METHODS: We reported two patients with autoimmune autonomic ganglionopathy (AAG) occurring as immune-related adverse events (irAEs). We also performed a review of previous case reports presenting dysautonomia during ICI therapy. Moreover, we conducted pharmacovigilance analyses using the US Food and Drug Administration Adverse Events Reporting System (FAERS) to investigate dysautonomia associated with ICI. RESULTS: Two patients in our care developed both AAG and autoimmune encephalitis following ICI therapy for lung cancers. We comprehensively reviewed 13 published cases (M:F = 11:2, mean onset age of 53 years) with ICI-associated dysautonomia including AAG (n = 3) and autonomic neuropathy (n = 10). Of these, ICI monotherapy was performed in seven and combination ICI use in six. In 6 of 13 patients, dysautonomia appeared within one month after the start of ICIs. Orthostatic hypotension was observed in 7 and urinary incontinence or retention in five. All patients except three showed gastrointestinal symptoms. Anti-ganglionic acetylcholine receptor antibodies were undetectable. All but two patients received immune-modulating therapy. Immuno-modulating therapy was effective in three patients with AAG and two patients with autonomic neuropathy, but ineffective in the others. Five patients died, of either the neurological irAE (n = 3) or cancer (n = 2). The pharmacovigilance analyses using FAERS showed that ipilimumab monotherapy and the combination of nivolumab and ipilimumab constituted significant risks for developing dysautonomia, consistent with the review of literature. CONCLUSION: ICIs can cause dysautonomia including AAG, and autonomic neuropathy is a neurological irAE.

Extracting Multiple Worries From Breast Cancer Patient Blogs Using Multilabel Classification With the Natural Language Processing Model Bidirectional Encoder Representations From Transformers: Infodemiology Study of Blogs.

BACKGROUND: Patients with breast cancer have a variety of worries and need multifaceted information support. Their accumulated posts on social media contain rich descriptions of their daily worries concerning issues such as treatment, family, and finances. It is important to identify these issues to help patients with breast cancer to resolve their worries and obtain reliable information. OBJECTIVE: This study aimed to extract and classify multiple worries from text generated by patients with breast cancer using Bidirectional Encoder Representations From Transformers (BERT), a context-aware natural language processing model. METHODS: A total of 2272 blog posts by patients with breast cancer in Japan were collected. Five worry labels, "treatment," "physical," "psychological," "work/financial," and "family/friends," were defined and assigned to each post. Multiple labels were allowed. To assess the label criteria, 50 blog posts were randomly selected and annotated by two researchers with medical knowledge. After the interannotator agreement had been assessed by means of Cohen kappa, one researcher annotated all the blogs. A multilabel classifier that simultaneously predicts five worries in a text was developed using BERT. This classifier was fine-tuned by using the posts as input and adding a classification layer to the pretrained BERT. The performance was evaluated for precision using the average of 5-fold cross-validation results. RESULTS: Among the blog posts, 477 included "treatment," 1138 included "physical," 673 included "psychological," 312 included "work/financial," and 283 included "family/friends." The interannotator agreement values were 0.67 for "treatment," 0.76 for "physical," 0.56 for "psychological," 0.73 for "work/financial," and 0.73 for "family/friends," indicating a high degree of agreement. Among all blog posts, 544 contained no label, 892 contained one label, and 836 contained multiple labels. It was found that the worries varied from user to user, and the worries posted by the same user changed over time. The model performed well, though prediction performance differed for each label. The values of precision were 0.59 for "treatment," 0.82 for "physical," 0.64 for "psychological," 0.67 for "work/financial," and 0.58 for "family/friends." The higher the interannotator agreement and the greater the number of posts, the higher the precision tended to be. CONCLUSIONS: This study showed that the BERT model can extract multiple worries from text generated from patients with breast cancer. This is the first application of a multilabel classifier using the BERT model to extract multiple worries from patient-generated text. The results will be helpful to identify breast cancer patients' worries and give them timely social support.

Latent class analysis of patients' background factors affecting the risk of specific adverse drug reactions to dipeptidyl peptidase 4 inhibitors.

BACKGROUND AND PURPOSE: Spontaneous reporting is widely used to identify adverse drug reactions (ADRs), but relatively little is known about the relationships between specific ADRs and background factors of affected patients. Here, we applied latent class analysis (LCA) to identify background factors associated with different ADRs in type 2 diabetes patients treated with dipeptidyl peptidase 4 (DPP-4) inhibitors, using the Japanese Adverse Drug Event Report (JADER) database. MATERIALS AND METHODS: Patients using only a DPP-4 inhibitor who encountered ADRs were selected from the JADER database up to April 2019 (N = 3,577). LCA was employed to classify these cases based on underlying diseases and lifestyle factors (alcohol, tobacco, diet, and exercise) and to identify characteristic ADRs in each class. The optimum number of classes was determined by selecting the model with the lowest value of the Bayesian information criterion (BIC). RESULTS: A six-class model had the lowest BIC, and these classes were characterized by specific background factors and ADRs. For example, one class included diabetes complications, while another class included exercise and diet as background factors. Increased risk of a specific ADR(s), such as pancreatitis or pemphigoid, was found in each class. The nine DPP-4 inhibitors were not uniformly distributed among the classes, though individual classes included patients receiving different inhibitors. CONCLUSION: Our findings indicate that characteristic background factors of patients experiencing specific DPP-4 inhibitor-induced ADRs reported in the JADER database are different and can be classified by LCA. This methodology may be useful for predicting ADRs not detected during drug development.

Identification of hand-foot syndrome from cancer patients' blog posts: BERT-based deep-learning approach to detect potential adverse drug reaction symptoms.

Early detection and management of adverse drug reactions (ADRs) is crucial for improving patients' quality of life. Hand-foot syndrome (HFS) is one of the most problematic ADRs for cancer patients. Recently, an increasing number of patients post their daily experiences to internet community, for example in blogs, where potential ADR signals not captured through routine clinic visits can be described. Therefore, this study aimed to identify patients with potential ADRs, focusing on HFS, from internet blogs by using natural language processing (NLP) deep-learning methods. From 10,646 blog posts, written in Japanese by cancer patients, 149 HFS-positive sentences were extracted after pre-processing, annotation and scrutiny by a certified oncology pharmacist. The HFS-positive sentences described not only HFS typical expressions like "pain" or "spoon nail", but also patient-derived unique expressions like onomatopoeic ones. The dataset was divided at a 4 to 1 ratio and used to train and evaluate three NLP deep-learning models: long short-term memory (LSTM), bidirectional LSTM and bidirectional encoder representations from transformers (BERT). The BERT model gave the best performance with precision 0.63, recall 0.82 and f1 score 0.71 in the HFS user identification task. Our results demonstrate that this NLP deep-learning model can successfully identify patients with potential HFS from blog posts, where patients' real wordings on symptoms or impacts on their daily lives are described. Thus, it should be feasible to utilize patient-generated text data to improve ADR management for individual patients.

Development of Risk Prediction Model for Grade 2 or Higher Hypocalcemia in Patients With Bone Metastasis Treated With Denosumab Plus Cholecalciferol (Vitamin D3 )/Calcium Supplement.

Denosumab-induced hypocalcemia is sometimes severe, and although a natural vitamin D/calcium combination is used to prevent hypocalcemia, some patients rapidly develop severe hypocalcemia even under supplementation. It is clinically important to predict this risk. This study aimed to develop a risk prediction model for grade ≥2 hypocalcemia within 28 days after the first denosumab dose under natural vitamin D/calcium supplementation. Using a large database containing multicenter practice data, 2399 patients with bone metastasis who were treated with denosumab between June 2013 and May 2020 were retrospectively analyzed. Background factors in patients who developed grade ≥2 hypocalcemia within 28 days after the first denosumab dose and those who did not were compared by univariate analysis. Multivariate analysis was conducted to develop a risk prediction model. The model was evaluated for discriminant performance (receiver operating characteristic-area under the curve, sensitivity, specificity) and predictive performance (calibration slope). A total of 124 patients in the hypocalcemia group and 1191 patients in the nonhypocalcemia group were extracted. A risk prediction model consisting of sex, calcium, albumin, alkaline phosphatase, osteoporosis, breast cancer, gastric cancer, proton pump inhibitor combination, and pretreatment with zoledronic acid was developed. The receiver operating characteristic-area under the curve was 0.87. Sensitivity and specificity were 83% and 81%, respectively, and the calibration slope indicated acceptable agreement between observed and predicted risk. This model appears to be useful to predict the risk of denosumab-induced hypocalcemia and thus should be helpful for risk management of denosumab treatment in patients with bone metastases.

Information provision and retrieval by registered salespersons from consumers during over-the-counter drug sales - a questionnaire survey.

BACKGROUND: In Japan, non-pharmacists who are accredited as registered salespersons can sell over-the-counter (OTC) drugs, and they play a very important role in supporting proper OTC drug use by consumers. The purpose of this study was to evaluate information provided to and information collected from consumers, and cooperation with pharmacists during OTC drug sales by registered salespersons, and to clarify their related concerns and behaviors. METHODS: A cross-sectional questionnaire-based survey of 385 registered salespersons working at 56 drugstores throughout Japan was conducted. Based on the questionnaire survey, the frequency of information provision/collection in various categories was determined for the registered salespersons. The relation between concerns of registered salespersons relating to OTC drug sales and the frequency of information provision/collection was examined. The frequency of consultation of registered salespersons with a pharmacist was calculated for registered salespersons with/without in-store pharmacists. The χ-square test or Fisher's exact test was performed to assess the significance of differences. RESULTS: Two hundred and seven registered salespersons (53.7%) responded completely. A greater number of OTC drug purchasers per day was associated with a greater frequency of information provision about "side effects" and information collection about "favorite items" (alcohol, tobacco, health foods, etc.) (p < 0.05). One hundred and thirty-nine (67.2%) participants had concerns about "interactions between OTC drugs and prescription drugs", and these concerns were related to the frequency of information provision/collection (p < 0.05). Regarding the frequency of consultation with a pharmacist, 35 of 46 participants (76.1%) working with pharmacists answered "always" or "usually", whereas only 19 of 161 participants (11.8%) working without full-time pharmacists answered "always" or "usually". More than half of the registered salespersons thought that cooperation with a pharmacist was necessary when they were "asked about concomitant use with prescription drugs" or "told that side effects happened." CONCLUSIONS: The results of this study show that experienced registered salespersons selling OTC drugs are more likely to collect information from consumers and to provide information to consumers. It appears to be important for registered salespersons to cooperate with pharmacists in order to provide and collect appropriate information about concomitant medications.

Risk factors for severe neutropenia in pancreatic cancer patients treated with gemcitabine/nab-paclitaxel combination therapy.

AIM: Combination therapy with gemcitabine and nanoparticle albumin-bound paclitaxel (nab-paclitaxel), known as GnP therapy, significantly prolongs the survival of pancreatic cancer patients compared with gemcitabine monotherapy. However, it may cause severe neutropenia, requiring discontinuation of treatment. This study aimed to clarify the risk factors for Grade 3/4 neutropenia during GnP therapy. METHODS: Clinical data of pancreatic cancer patients who underwent GnP therapy at the Cancer Institute Hospital of the Japanese Foundation for Cancer Research from December 2014 to December 2016 were retrospectively collected. The relationship of Grade 3/4 neutropenia onset to laboratory values and patient background factors was investigated by multivariate logistic regression analysis. RESULTS: Clinical data of 222 patients were analyzed. Grade 3/4 neutropenia occurred in 118 patients (53.2%) in the first cycle of GnP therapy. Multivariate analysis identified low absolute neutrophil count (ANC), high total bilirubin (T-Bil), and low C-reactive protein (CRP) as risk factors for Grade 3/4 neutropenia. Age was not a risk factor. The incidence of neutropenia was 85.7% in patients with all three risk factors, but only 27.7% in patients with none of them. CONCLUSION: Low ANC, high T-Bil, and low CRP may be risk factors for Grade 3/4 neutropenia in patients receiving GnP therapy, even if these laboratory values are within normal reference ranges. Patients with these risk factors should be carefully monitored for adverse events.

Pharmacokinetic model analysis of interaction between phenytoin and capecitabine.

OBJECTIVE: Recent reports have shbown an increase in serum phenytoin levels resulting in phenytoin toxicity after initiation of luoropyrimidine chemotherapy. To prevent phenytoin intoxication, phenytoin dosage must be adjusted. We sought to develop a pharmacokinetic model of the interaction between phenytoin and capecitabine. METHODS: We developed the phenytoin-capecitabine interaction model on the assumption that fluorouracil (5-FU) inhibits cytochrome P450 (CYP) 2C9 synthesis in a concentration- dependent manner. The plasma 5-FU concentration after oral administration of capecitabine was estimated using a conventional compartment model. Nonlinear pharmacokinetics of phenytoin was modeled by incorporating the Michaelis-Menten equation to represent the saturation of phenytoin metabolism. The resulting model was fitted to data from our previously-reported cases. RESULTS: The developed phenytoincapecitabine interaction model successfully described the profiles of serum phenytoin concentration in patients who received phenytoin and capecitabine concomitantly. The 50% inhibitory 5-FU concentration for CYP2C9 synthesis and the degradation rate constant of CYP2C9 were estimated to be 0.00310 ng/mL and 0.0768 day-1, respectively. This model and these parameters allow us to predict the appropriate phenytoin dosage schedule when capecitabine is administered concomitantly. CONCLUSIONS: This newly-developed model accurately describes changes in phenytoin concentration during concomitant capecitabine chemotherapy, and it may be clinically useful for predicting appropriate phenytoin dosage adjustments for maintaining serum phenytoin levels within the therapeutic range.

Sequential interaction of phenytoin and phenobarbital with fluorouracil.

OBJECTIVE: We report a case of sequential interaction of phenytoin (PHT) and phenobarbital (PB) with fluorouracil (5-FU). CASE REPORT: A male patient aged 60 under treatment with PHT and PB in which serum concentrations of PHT (32.8 µg/ml) and PB (26.7 µg/ml) increased ~ 2-fold after the start of postoperative adjuvant therapy with calcium levofolinate (l-LV) and fluorouracil (5-FU). When the drug interactions of this case evaluated using the Drug Interaction Probability Scale, was assessed as "probable". DISCUSSION: In this case, 5-FU increased PHT, which in turn may have increased the PB concentration, suggesting that when fluoropyrimidine antitumor agents are administered to patients receiving PHT in combination with other drugs, some measures should be taken in consideration of secondary effects of antitumor agents on other drugs that may possibly interact with PHT, including frequent monitoring of blood drug concentration.

Inhibitory effects of herbal extracts on breast cancer resistance protein (BCRP) and structure-inhibitory potency relationship of isoflavonoids.

The inhibition of intestinal breast cancer resistance protein (BCRP), which restricts the absorption of xenobiotics, may increase the systemic availability of its substrates. The aim of this study was to evaluate the inhibitory effects of herbal extracts and their constituents on BCRP-mediated transport. The inhibitory effects of 9 herbal extracts and 23 isoflavonoids, including soybean-derived isoflavones, on BCRP-mediated methotrexate (MTX) transport were evaluated using BCRP-expressing membrane vesicles. The structure-inhibitory potency relationship was investigated by multiple factor analysis. Extracts of soybean, Gymnema sylvestre, black cohosh and passion flower and rutin strongly inhibited BCRP-mediated transport of MTX at 1 mg/ml, while inhibition by chlorella, milk thistle and Siberian ginseng extracts was weak. Among the 23 isoflavonoids examined, all of which inhibited BCRP-mediated transport, coumestrol showed the most potent inhibition (IC(50)=63 nM). The inhibitory potencies of 6 isoflavonoid glucosides were 10- to 100-fold lower than those of the corresponding aglycones. The addition of a 5-hydroxyl or 6-methoxyl moiety tended to potentiate the inhibition. The inhibitory potency of daidzein was decreased 100-fold by 7-glucuronidation, but was virtually unaffected by 4'-sulfation. Thus, some herbal and dietary supplements and isoflavonoids may increase the systemic availability of BCRP substrates when concomitantly given orally.

Saturable binding of finasteride to steroid 5alpha-reductase as determinant of nonlinear pharmacokinetics.

Finasteride, a steroid 5alpha-reductase (5alphaR) inhibitor, is used to treat benign prostatic hyperplasia and androgenetic alopecia. We aimed to develop a pharmacokinetic/pharmacodynamic model to explain its nonlinear pharmacokinetics and describe the serum concentration profile of dihydrotestosterone (DHT) after finasteride administration. We developed a pharmacokinetic model incorporating a compartment that represents the binding of finasteride to 5alphaR. We fitted this model to the time-concentration profiles of finasteride after repeated administration of finasteride 0.2 and 1 mg/day. We constructed a pharmacodynamic model considering the inhibition of 5alphaR type I and type II (5alphaR1 and 5alphaR2). This model was fitted to the time profiles of serum DHT. The developed pharmacokinetic model well described nonlinear increase in AUC after repeated administration of finasteride. The association and dissociation rate constants were estimated to be 0.0293/nmol/hr and 0.0185/hr, respectively. Pharmacodynamic model analysis suggested that the 5alphaR1 inhibition is dose-dependent in the dose range from 0.2 to 100 mg, while the 5alphaR2 inhibition is almost saturated in the same dose range. Finasteride's saturable binding to 5alphaR2 is the likely cause of its nonlinear pharmacokinetics. The developed pharmacokinetic/pharmacodynamic model should allow prediction of plasma concentration profiles of finasteride and DHT.