Yifan Yang, Lianlian Bian, Yuan Cheng, Yan Xu, Hui Shao, Jian Rao, Sixiang Ge, Jifang Gong, Min Jiang, Xiaoyu Zheng, Lijun Liu, Shihui Ma, Xuan Liu, Tao Cheng, Chenyan Gao
As cutting-edge technologies in biomedicine, cell and gene therapy (CGT) products demonstrate immense potential in treating cancer, rare diseases, and genetic disorders, thereby driving the importance of clinical research in this area. This study analyzes the growth trends and key characteristics of 1033 Investigator-Initiated Trials (IITs) conducted by mainland Chinese institutions in the CGT field. The results show that IITs have played a positive role in the early proof-of-concept of CGT products, helping to obtain preliminary safety and efficacy data, and exploring the combination of CGT products with other therapies. Additionally, this study discusses the regional distribution, therapeutic areas, and challenges faced by IITs in the development of CGT products in China. Based on these findings, policy suggestions are proposed to optimize the regulation of IITs in mainland China, such as improving regulatory frameworks and enhancing technical guidance. It is hoped that these measures will further improve the efficiency and quality of IITs, fully utilize the large patient base and abundant clinical resources, and support the development of high-quality CGT products in mainland China.
{"title":"The Role and Challenges of Investigator-Initiated Trials in the Cell and Gene Therapy Products Boom in Mainland China","authors":"Yifan Yang, Lianlian Bian, Yuan Cheng, Yan Xu, Hui Shao, Jian Rao, Sixiang Ge, Jifang Gong, Min Jiang, Xiaoyu Zheng, Lijun Liu, Shihui Ma, Xuan Liu, Tao Cheng, Chenyan Gao","doi":"10.1111/cts.70148","DOIUrl":"https://doi.org/10.1111/cts.70148","url":null,"abstract":"<p>As cutting-edge technologies in biomedicine, cell and gene therapy (CGT) products demonstrate immense potential in treating cancer, rare diseases, and genetic disorders, thereby driving the importance of clinical research in this area. This study analyzes the growth trends and key characteristics of 1033 Investigator-Initiated Trials (IITs) conducted by mainland Chinese institutions in the CGT field. The results show that IITs have played a positive role in the early proof-of-concept of CGT products, helping to obtain preliminary safety and efficacy data, and exploring the combination of CGT products with other therapies. Additionally, this study discusses the regional distribution, therapeutic areas, and challenges faced by IITs in the development of CGT products in China. Based on these findings, policy suggestions are proposed to optimize the regulation of IITs in mainland China, such as improving regulatory frameworks and enhancing technical guidance. It is hoped that these measures will further improve the efficiency and quality of IITs, fully utilize the large patient base and abundant clinical resources, and support the development of high-quality CGT products in mainland China.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70148","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143389073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
James T. Nguyen, Christopher M. Arian, Rakshit S. Tanna, Maxey G. Cherel, Matthew E. Layton, John R. White, Kenneth E. Thummel, Mary F. Paine
Pharmacokinetic drug interactions can lead to unexpected changes in plasma concentrations of the object drug, potentially increasing the risk for adverse effects and/or decreasing therapeutic efficacy. The botanical product goldenseal was previously shown to decrease metformin systemic exposure in healthy adults. This three-arm, open-label, crossover clinical study assessed the pharmacokinetic goldenseal–metformin interaction in adults with type 2 diabetes stabilized on therapeutic doses of metformin (500–2550 mg daily). The aggregate pharmacokinetic data indicated no clinically meaningful interaction as determined by the metformin area under the plasma concentration-time curve (AUC) geometric mean ratio [90% confidence interval] of 0.93 [0.86–1.01] laying within the predefined no-effect range (0.80–1.25). However, metformin AUC decreased by ~20%, 14%, and 0% after goldenseal coadministration at low (500–750 mg), moderate (1000–1500 mg), and high (2000–2550 mg) metformin doses, respectively; renal clearance and half-life remained unchanged throughout. The exploratory pharmacodynamic endpoint, HbA1c, decreased on average from 6.8% to 6.5%, regardless of the effects of goldenseal on metformin pharmacokinetics. The decreasing effect of goldenseal on metformin systemic exposure with increasing metformin dose, coupled with no changes in renal excretion and elimination half-life, indicated that both the pharmacokinetic goldenseal–metformin interaction and the nonlinear absorption of metformin are governed by saturable, intestinal transport mechanism(s). The disconnect between changes in metformin systemic exposure and therapeutic effects emphasizes the need to evaluate clinical biomarkers to comprehensively assess drug interaction risks, particularly those involving natural products. Healthcare providers may consider cautioning patients about supplementing metformin pharmacotherapy with goldenseal to avoid risks for undesired changes in glycemic control.
{"title":"The Pharmacokinetic Interaction Between Metformin and the Natural Product Goldenseal Is Metformin Dose-Dependent: A Three-Arm Crossover Study in Adults With Type 2 Diabetes","authors":"James T. Nguyen, Christopher M. Arian, Rakshit S. Tanna, Maxey G. Cherel, Matthew E. Layton, John R. White, Kenneth E. Thummel, Mary F. Paine","doi":"10.1111/cts.70120","DOIUrl":"https://doi.org/10.1111/cts.70120","url":null,"abstract":"<p>Pharmacokinetic drug interactions can lead to unexpected changes in plasma concentrations of the object drug, potentially increasing the risk for adverse effects and/or decreasing therapeutic efficacy. The botanical product goldenseal was previously shown to decrease metformin systemic exposure in healthy adults. This three-arm, open-label, crossover clinical study assessed the pharmacokinetic goldenseal–metformin interaction in adults with type 2 diabetes stabilized on therapeutic doses of metformin (500–2550 mg daily). The aggregate pharmacokinetic data indicated no clinically meaningful interaction as determined by the metformin area under the plasma concentration-time curve (AUC) geometric mean ratio [90% confidence interval] of 0.93 [0.86–1.01] laying within the predefined no-effect range (0.80–1.25). However, metformin AUC decreased by ~20%, 14%, and 0% after goldenseal coadministration at low (500–750 mg), moderate (1000–1500 mg), and high (2000–2550 mg) metformin doses, respectively; renal clearance and half-life remained unchanged throughout. The exploratory pharmacodynamic endpoint, HbA1c, decreased on average from 6.8% to 6.5%, regardless of the effects of goldenseal on metformin pharmacokinetics. The decreasing effect of goldenseal on metformin systemic exposure with increasing metformin dose, coupled with no changes in renal excretion and elimination half-life, indicated that both the pharmacokinetic goldenseal–metformin interaction and the nonlinear absorption of metformin are governed by saturable, intestinal transport mechanism(s). The disconnect between changes in metformin systemic exposure and therapeutic effects emphasizes the need to evaluate clinical biomarkers to comprehensively assess drug interaction risks, particularly those involving natural products. Healthcare providers may consider cautioning patients about supplementing metformin pharmacotherapy with goldenseal to avoid risks for undesired changes in glycemic control.</p><p><b>Trial Registration:</b> ClinicalTrials.gov identifier: NCT05081583</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70120","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143396787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CAR-T cell therapy, renowned for its success in oncology, is now venturing into the realm of B cell-mediated autoimmune diseases. Recent observations have revealed significant pharmacological effects of CD19 CAR-T cells in patients with systemic lupus erythematosus (SLE), suggesting promising applications in other autoimmune conditions. Consequently, as of December 2024, there are 116 different clinical trials evaluating CAR-T cells against autoimmune conditions. While the field is starting to understand the overall pharmacological actions of CAR-T cells in autoimmune diseases, the dose-exposure-response relationship remains inadequately characterized due to limited clinical data. To address these uncertainties, we have developed a Quantitative Systems Pharmacology (QSP) model using short-term limited clinical data of anti-CD19 CAR-Ts in autoimmune disease patients (n = 5), followed by a model qualification step utilizing an external dataset (n = 13). The developed QSP model integrated and effectively characterized the (1) cellular kinetics of different immunophenotypic population of CAR-T cells, (2) impact of lymphodepletion chemotherapy on host immune cells, (3) CAR-mediated elimination of CD19+ B-cells and (4) dynamic changes in disease surrogate biomarkers and its relationship with clinical score. The key pharmacological biomarkers which were incorporated within the QSP model included anti double stranded DNA (anti-dsDNA) antibodies, proteinuria, C3 protein and IFN-alpha. Later, a linear regression analysis-based relationship was developed between continuous disease biomarkers and the categorical SLE disease activity index (SLE-DAI) determined by the investigators offering a predictive framework for disease progression in SLE patients. This proposed QSP model holds potential to elucidate quantitative pharmacology and expedite clinical advancement of autologous and allogeneic cell therapies in autoimmune diseases.
{"title":"Mechanistic Evaluation of Anti-CD19 CAR-T Cell Therapy Repurposed in Systemic Lupus Erythematosus Using a Quantitative Systems Pharmacology Model","authors":"Hyunseo Park, Ganesh M. Mugundu, Aman P. Singh","doi":"10.1111/cts.70146","DOIUrl":"https://doi.org/10.1111/cts.70146","url":null,"abstract":"<p>CAR-T cell therapy, renowned for its success in oncology, is now venturing into the realm of B cell-mediated autoimmune diseases. Recent observations have revealed significant pharmacological effects of CD19 CAR-T cells in patients with systemic lupus erythematosus (SLE), suggesting promising applications in other autoimmune conditions. Consequently, as of December 2024, there are 116 different clinical trials evaluating CAR-T cells against autoimmune conditions. While the field is starting to understand the overall pharmacological actions of CAR-T cells in autoimmune diseases, the dose-exposure-response relationship remains inadequately characterized due to limited clinical data. To address these uncertainties, we have developed a Quantitative Systems Pharmacology (QSP) model using short-term limited clinical data of anti-CD19 CAR-Ts in autoimmune disease patients (<i>n</i> = 5), followed by a model qualification step utilizing an external dataset (<i>n</i> = 13). The developed QSP model integrated and effectively characterized the (1) cellular kinetics of different immunophenotypic population of CAR-T cells, (2) impact of lymphodepletion chemotherapy on host immune cells, (3) CAR-mediated elimination of CD19+ B-cells and (4) dynamic changes in disease surrogate biomarkers and its relationship with clinical score. The key pharmacological biomarkers which were incorporated within the QSP model included anti double stranded DNA (anti-dsDNA) antibodies, proteinuria, C3 protein and IFN-alpha. Later, a linear regression analysis-based relationship was developed between continuous disease biomarkers and the categorical SLE disease activity index (SLE-DAI) determined by the investigators offering a predictive framework for disease progression in SLE patients. This proposed QSP model holds potential to elucidate quantitative pharmacology and expedite clinical advancement of autologous and allogeneic cell therapies in autoimmune diseases.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70146","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143389220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tallal Mushtaq Hashmi, Mushood Ahmed, Ali Haider, Salman Naseem, Uzair Jafar, Munir Hussain, Javed Iqbal, Waqar Ali, Raheel Ahmed
The effectiveness of glucagon-like peptide-1 receptor agonists in facilitating weight loss among patients with diabetes is widely recognized. However, there are limited data available on the relative effectiveness and safety of once-weekly semaglutide versus once-daily liraglutide. We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) identified through a comprehensive search of the Cochrane Library, PubMed, and ScienceDirect databases from inception until July 2024. Statistical analysis was conducted using R version 4.4.1 with the “meta” package, employing a random effects model. Three RCTs with a total of 922 patients were included in our meta-analysis. The results indicated that OW semaglutide significantly reduced body weight (WMD: −4.55; 95% CI: −6.43, −2.67, p < 0.01), HbA1c (WMD: −0.46; 95% CI: −0.84, −0.08; p = 0.02), and fasting plasma glucose levels (WMD: −1.23; 95% CI: −1.51, −0.95; p < 0.01) in comparison to OD liraglutide. The risk of severe adverse effects (OR, 1.66; 95% CI, 0.53–5.16; p = 0.38) and gastrointestinal adverse effects (OR, 1.84; 95% CI, 0.82–4.14; p = 0.14) was comparable between both groups. Once-weekly semaglutide therapy results in a more pronounced loss in body weight, HbA1c, and fasting glucose levels compared to once-daily liraglutide.
{"title":"Once-Weekly Semaglutide Versus Once-Daily Liraglutide for Weight Loss in Adults: A Meta-Analysis of Randomized Controlled Trials","authors":"Tallal Mushtaq Hashmi, Mushood Ahmed, Ali Haider, Salman Naseem, Uzair Jafar, Munir Hussain, Javed Iqbal, Waqar Ali, Raheel Ahmed","doi":"10.1111/cts.70127","DOIUrl":"https://doi.org/10.1111/cts.70127","url":null,"abstract":"<p>The effectiveness of glucagon-like peptide-1 receptor agonists in facilitating weight loss among patients with diabetes is widely recognized. However, there are limited data available on the relative effectiveness and safety of once-weekly semaglutide versus once-daily liraglutide. We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) identified through a comprehensive search of the Cochrane Library, PubMed, and ScienceDirect databases from inception until July 2024. Statistical analysis was conducted using R version 4.4.1 with the “meta” package, employing a random effects model. Three RCTs with a total of 922 patients were included in our meta-analysis. The results indicated that OW semaglutide significantly reduced body weight (WMD: −4.55; 95% CI: −6.43, −2.67, <i>p</i> < 0.01), HbA1c (WMD: −0.46; 95% CI: −0.84, −0.08; <i>p</i> = 0.02), and fasting plasma glucose levels (WMD: −1.23; 95% CI: −1.51, −0.95; <i>p</i> < 0.01) in comparison to OD liraglutide. The risk of severe adverse effects (OR, 1.66; 95% CI, 0.53–5.16; <i>p</i> = 0.38) and gastrointestinal adverse effects (OR, 1.84; 95% CI, 0.82–4.14; <i>p</i> = 0.14) was comparable between both groups. Once-weekly semaglutide therapy results in a more pronounced loss in body weight, HbA1c, and fasting glucose levels compared to once-daily liraglutide.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70127","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kevin Lam, John T. Mondick, Gary Peltz, Manhong Wu, Walter K. Kraft
Ondansetron is an anti-emetic 5-HT3 receptor antagonist being investigated for treating neonatal opioid withdrawal syndrome (NOWS). Sparse PK data were analyzed from a multicenter, double-blind clinical trial with 98 mother/neonate dyads. Pregnant women with opioid use disorder were randomized to receive either placebo or ondansetron 8 mg intravenously within 4 h of delivery. Neonates born to mothers who were randomized to ondansetron received 0.07 mg/kg orally once every 24 h for up to five doses. Using current PK data, model parameters from a two-compartmental structural model from the literature (i.e., a priori model) were updated with the Metropolis-Hastings Markov-chain Monte Carlo estimation algorithm in NONMEM. The updated Bayesian model indicated a slower absorption rate (KA) but no differences in model parameters (CL, V, V2, Q) after including body weight and postmenstrual age. Sensitivity analyses on CL prior revealed statistical improvement favoring larger body weights, but not changes in postmenstrual age. However, further model development using larger body weights did not illustrate superior performance through visual inspection of diagnostic plots. Overall, a cumulative AUC of at least 1000 ng*h/mL appears to be the threshold for reductions in symptom severity. Exposure-response analyses suggest the total number of doses to be the primary driver for efficacy with respect to AUC, which reasonably aligns with the literature. Overall, it is suggested that at least three doses of the current oral ondansetron regimen are required to reduce symptom severity in neonates.
{"title":"Bayesian Population Pharmacokinetic Modeling of Ondansetron for Neonatal Opioid Withdrawal Syndrome","authors":"Kevin Lam, John T. Mondick, Gary Peltz, Manhong Wu, Walter K. Kraft","doi":"10.1111/cts.70147","DOIUrl":"https://doi.org/10.1111/cts.70147","url":null,"abstract":"<p>Ondansetron is an anti-emetic 5-HT3 receptor antagonist being investigated for treating neonatal opioid withdrawal syndrome (NOWS). Sparse PK data were analyzed from a multicenter, double-blind clinical trial with 98 mother/neonate dyads. Pregnant women with opioid use disorder were randomized to receive either placebo or ondansetron 8 mg intravenously within 4 h of delivery. Neonates born to mothers who were randomized to ondansetron received 0.07 mg/kg orally once every 24 h for up to five doses. Using current PK data, model parameters from a two-compartmental structural model from the literature (i.e., a priori model) were updated with the Metropolis-Hastings Markov-chain Monte Carlo estimation algorithm in NONMEM. The updated Bayesian model indicated a slower absorption rate (KA) but no differences in model parameters (CL, V, V2, Q) after including body weight and postmenstrual age. Sensitivity analyses on CL prior revealed statistical improvement favoring larger body weights, but not changes in postmenstrual age. However, further model development using larger body weights did not illustrate superior performance through visual inspection of diagnostic plots. Overall, a cumulative AUC of at least 1000 ng*h/mL appears to be the threshold for reductions in symptom severity. Exposure-response analyses suggest the total number of doses to be the primary driver for efficacy with respect to AUC, which reasonably aligns with the literature. Overall, it is suggested that at least three doses of the current oral ondansetron regimen are required to reduce symptom severity in neonates.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70147","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amitava Mitra, Julie Mackey Ahsan, Marilyn Tabachri, Taha El-Shahat, Mollie Leoni, Stephen Dale
Ziftomenib, a potent, selective inhibitor that binds menin at the lysine methyltransferase 2 interaction site, has demonstrated promising clinical activity with manageable toxicity in heavily pretreated patients with acute myeloid leukemia (AML) and nucleophosmin 1 mutations. This phase 1, open-label study characterized the absorption, metabolism, excretion, and bioavailability of ziftomenib in healthy men and comprised two parts. In part A, a single oral dose of ziftomenib 400 mg (containing 250 μCi [14C]-ziftomenib) was given to evaluate routes and rates of elimination, total radioactivity, and other pharmacokinetic parameters. In part B, a single oral dose of ziftomenib 400 mg followed by an intravenous dose of ziftomenib < 100 μg (containing 1 μCi [14C]-ziftomenib) was administered to evaluate absolute bioavailability (both n = 8 patients). A median tmax of 3.5 h and an elimination t1/2 of 61.5 h demonstrated rapid ziftomenib absorption and enabled once-daily dosing. Total radioactivity recovery was 89.7% in feces and 0.5% in urine over 480 h. Absolute bioavailability of 12.9% was observed. Ziftomenib was primarily metabolized by oxidation, N-demethylation, and N-dealkylation, with 19 metabolites recovered in plasma. All metabolites were considered minor (< 10% of total drug-related exposure). Ziftomenib was the most abundant plasma component (> 10% of total drug-related exposure). In conclusion, ziftomenib underwent limited metabolism following absorption and was primarily excreted as unchanged parent drug in feces. Ziftomenib was well tolerated with no new safety concerns in healthy men. Considering the pharmacokinetic profile and manageable safety outcomes, these findings support further clinical investigation of ziftomenib as treatment for AML.
{"title":"Pharmacokinetics and ADME Characterization After Oral and Intravenous Administration of [14C]-Ziftomenib in Healthy Male Participants","authors":"Amitava Mitra, Julie Mackey Ahsan, Marilyn Tabachri, Taha El-Shahat, Mollie Leoni, Stephen Dale","doi":"10.1111/cts.70153","DOIUrl":"https://doi.org/10.1111/cts.70153","url":null,"abstract":"<p>Ziftomenib, a potent, selective inhibitor that binds menin at the lysine methyltransferase 2 interaction site, has demonstrated promising clinical activity with manageable toxicity in heavily pretreated patients with acute myeloid leukemia (AML) and <i>nucleophosmin 1</i> mutations. This phase 1, open-label study characterized the absorption, metabolism, excretion, and bioavailability of ziftomenib in healthy men and comprised two parts. In part A, a single oral dose of ziftomenib 400 mg (containing 250 μCi [<sup>14</sup>C]-ziftomenib) was given to evaluate routes and rates of elimination, total radioactivity, and other pharmacokinetic parameters. In part B, a single oral dose of ziftomenib 400 mg followed by an intravenous dose of ziftomenib < 100 μg (containing 1 μCi [<sup>14</sup>C]-ziftomenib) was administered to evaluate absolute bioavailability (both <i>n</i> = 8 patients). A median t<sub>max</sub> of 3.5 h and an elimination t<sub>1/2</sub> of 61.5 h demonstrated rapid ziftomenib absorption and enabled once-daily dosing. Total radioactivity recovery was 89.7% in feces and 0.5% in urine over 480 h. Absolute bioavailability of 12.9% was observed. Ziftomenib was primarily metabolized by oxidation, <i>N</i>-demethylation, and <i>N</i>-dealkylation, with 19 metabolites recovered in plasma. All metabolites were considered minor (< 10% of total drug-related exposure). Ziftomenib was the most abundant plasma component (> 10% of total drug-related exposure). In conclusion, ziftomenib underwent limited metabolism following absorption and was primarily excreted as unchanged parent drug in feces. Ziftomenib was well tolerated with no new safety concerns in healthy men. Considering the pharmacokinetic profile and manageable safety outcomes, these findings support further clinical investigation of ziftomenib as treatment for AML.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70153","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gerald C. So, Jessica Bo Li Lu, Ying-Hua Cheng, Debora L. Gisch, Sachiko Koyama, Ricardo Melo Ferreira, Travis R. Beamon, Zeruesenay Desta, Michael T. Eadon
Drug interactions are major causes of interindividual variability in tacrolimus exposure and effect. Tacrolimus, a widely used drug in transplant patients, is metabolized by CYP3A4 and CYP3A5. Cannabidiol (CBD) use after transplant is common. Clinical cases suggest CBD may alter tacrolimus exposure, but the mechanism of this interaction is unknown. We hypothesize that cannabidiol will inhibit tacrolimus metabolism in vitro mainly through CYP3A5 inhibition. In pooled human liver microsomes (HLMs) and recombinant (r) CYP3A4 and CYP3A5 enzymes, tacrolimus (1 μM) metabolism was determined using substrate depletion method in the absence (control) and the presence of 10 μM CBD, 7-hydroxyCBD, and 7-carboxyCBD. Ketoconazole (1 μM) served as a positive control for the inhibition of CYP3A. Linear regression analyses were performed to obtain kinetic parameters of the depletion. Tacrolimus depletion half-life was 2.54, 0.922, and 0.351 min with pooled HLMs, rCYP3A4, and rCYP3A5, respectively. In pooled HLMs, CBD and 7-hydroxyCBD increased tacrolimus half-life by 0.8- and 2.3-fold (both p < 0.0001), respectively. In rCYP3A4, CBD, 7-hydroxyCBD, and ketoconazole prolonged tacrolimus half-life by 5.8-, 14-, and 7.7-fold, respectively. In rCYP3A5, CBD, 7-hydroxyCBD, and ketoconazole prolonged half-life by 29.3-, 19.7-, and 0.1-fold, respectively. In all experiments, 7-carboxyCBD had minimal effect on tacrolimus depletion. CBD and 7-hydroxyCBD inhibited tacrolimus metabolism in vitro. CBD showed stronger inhibition in rCYP3A5 than rCYP3A4. The demonstrated CYP3A5 selectivity of cannabidiol may contribute to the in vitro identification of CYP3A5 substrates in new drug development. Our results support the potential of a clinical drug–drug interaction between CBD and tacrolimus.
{"title":"Inhibition of Tacrolimus Metabolism by Cannabidiol and Its Metabolites In Vitro","authors":"Gerald C. So, Jessica Bo Li Lu, Ying-Hua Cheng, Debora L. Gisch, Sachiko Koyama, Ricardo Melo Ferreira, Travis R. Beamon, Zeruesenay Desta, Michael T. Eadon","doi":"10.1111/cts.70152","DOIUrl":"https://doi.org/10.1111/cts.70152","url":null,"abstract":"<p>Drug interactions are major causes of interindividual variability in tacrolimus exposure and effect. Tacrolimus, a widely used drug in transplant patients, is metabolized by CYP3A4 and CYP3A5. Cannabidiol (CBD) use after transplant is common. Clinical cases suggest CBD may alter tacrolimus exposure, but the mechanism of this interaction is unknown. We hypothesize that cannabidiol will inhibit tacrolimus metabolism in vitro mainly through CYP3A5 inhibition. In pooled human liver microsomes (HLMs) and recombinant (r) CYP3A4 and CYP3A5 enzymes, tacrolimus (1 μM) metabolism was determined using substrate depletion method in the absence (control) and the presence of 10 μM CBD, 7-hydroxyCBD, and 7-carboxyCBD. Ketoconazole (1 μM) served as a positive control for the inhibition of CYP3A. Linear regression analyses were performed to obtain kinetic parameters of the depletion. Tacrolimus depletion half-life was 2.54, 0.922, and 0.351 min with pooled HLMs, rCYP3A4, and rCYP3A5, respectively. In pooled HLMs, CBD and 7-hydroxyCBD increased tacrolimus half-life by 0.8- and 2.3-fold (both <i>p</i> < 0.0001), respectively. In rCYP3A4, CBD, 7-hydroxyCBD, and ketoconazole prolonged tacrolimus half-life by 5.8-, 14-, and 7.7-fold, respectively. In rCYP3A5, CBD, 7-hydroxyCBD, and ketoconazole prolonged half-life by 29.3-, 19.7-, and 0.1-fold, respectively. In all experiments, 7-carboxyCBD had minimal effect on tacrolimus depletion. CBD and 7-hydroxyCBD inhibited tacrolimus metabolism in vitro. CBD showed stronger inhibition in rCYP3A5 than rCYP3A4. The demonstrated CYP3A5 selectivity of cannabidiol may contribute to the in vitro identification of CYP3A5 substrates in new drug development. Our results support the potential of a clinical drug–drug interaction between CBD and tacrolimus.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70152","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Max Smith, Rut Beyene, Paul Kolm, Theresa A. Young, Sara Zifa, Victoria Natividad, Andrea Licata, Robert H. Podolsky, Troy Moore, Richard Walsh, Shikha Deva, Alexander D. Walker, Michael B. Jacobs, Beth N. Peshkin, Sandra M. Swain
This trial aimed to identify the effects of providing pharmacogenomic (PGx) results and recommendations for patients with chronic pain treated in primary care practices compared to standard care. An open-label, prospective, largely virtual, type-2 hybrid effectiveness trial randomized participants to PGx or standard care arms. Adults with pain ≥ 3 months who were treated with tramadol, codeine, or hydrocodone enrolled. Alternative analgesics were recommended for CYP2D6 intermediate or poor metabolizers (IM/PMs). Prescribing decisions were at providers' discretion. The trial randomized 253 participants. A modified intent-to-treat primary analysis assessed change in pain intensity over 3 months among IM/PMs (PGx: 49; Standard care: 57). The PGx and standard care arms showed no difference in pain intensity change (−0.10 ± 0.63 vs. −0.21 ± 0.75 standard deviation; p = 0.74) or PGx-aligned care (69% vs. 63%; standardized difference [SD] = 0.13). In IM/PMs, secondary analyses of pain intensity change suggested improvements with PGx-aligned (n = 70; −0.21 ± 0.70) vs. unaligned care (n = 36; −0.06 ± 0.69) (SD = −0.22), with this difference increasing when examining IM/PMs with an analgesic change (aligned: n = 31, −0.28 ± 0.76; unaligned: n = 36, −0.06 ± 0.69; SD = −0.31). This approach to PGx implementation for chronic pain was not associated with different prescribing (i.e., similar proportions of PGx-aligned care) or clinical outcomes. Secondary analyses suggest that prescribing aligned with PGx recommendations showed a small improvement in pain intensity. However, the proportion of patients with a clinically meaningful improvement (≥ 30%) in pain intensity was similar. Future efforts should identify effective implementation methods.
{"title":"A Randomized Hybrid-Effectiveness Trial Comparing Pharmacogenomics (PGx) to Standard Care: The PGx Applied to Chronic Pain Treatment in Primary Care (PGx-ACT) Trial","authors":"D. Max Smith, Rut Beyene, Paul Kolm, Theresa A. Young, Sara Zifa, Victoria Natividad, Andrea Licata, Robert H. Podolsky, Troy Moore, Richard Walsh, Shikha Deva, Alexander D. Walker, Michael B. Jacobs, Beth N. Peshkin, Sandra M. Swain","doi":"10.1111/cts.70154","DOIUrl":"https://doi.org/10.1111/cts.70154","url":null,"abstract":"<p>This trial aimed to identify the effects of providing pharmacogenomic (PGx) results and recommendations for patients with chronic pain treated in primary care practices compared to standard care. An open-label, prospective, largely virtual, type-2 hybrid effectiveness trial randomized participants to PGx or standard care arms. Adults with pain ≥ 3 months who were treated with tramadol, codeine, or hydrocodone enrolled. Alternative analgesics were recommended for CYP2D6 intermediate or poor metabolizers (IM/PMs). Prescribing decisions were at providers' discretion. The trial randomized 253 participants. A modified intent-to-treat primary analysis assessed change in pain intensity over 3 months among IM/PMs (PGx: 49; Standard care: 57). The PGx and standard care arms showed no difference in pain intensity change (−0.10 ± 0.63 vs. −0.21 ± 0.75 standard deviation; <i>p</i> = 0.74) or PGx-aligned care (69% vs. 63%; standardized difference [SD] = 0.13). In IM/PMs, secondary analyses of pain intensity change suggested improvements with PGx-aligned (<i>n</i> = 70; −0.21 ± 0.70) vs. unaligned care (<i>n</i> = 36; −0.06 ± 0.69) (SD = −0.22), with this difference increasing when examining IM/PMs with an analgesic change (aligned: <i>n</i> = 31, −0.28 ± 0.76; unaligned: <i>n</i> = 36, −0.06 ± 0.69; SD = −0.31). This approach to PGx implementation for chronic pain was not associated with different prescribing (i.e., similar proportions of PGx-aligned care) or clinical outcomes. Secondary analyses suggest that prescribing aligned with PGx recommendations showed a small improvement in pain intensity. However, the proportion of patients with a clinically meaningful improvement (≥ 30%) in pain intensity was similar. Future efforts should identify effective implementation methods.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70154","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p>AI has revolutionized the drug discovery space in recent years, with applications ranging from highly accurate structure predictions of proteins [<span>1</span>], to the design and optimization of both small and large molecules [<span>2</span>]. Several large foundational models have been developed for encoding functional information of proteins in a powerful way to support the drug development pipeline [<span>3, 4</span>]. Figure 1 highlights the areas in the pipeline where AI now plays a significant role and is poised to disrupt traditional experimental techniques. The culmination of AI-driven discovery is de novo design, where the entire preclinical pipeline can be performed in silico, resulting in billions of dollars of R&D cost savings, translating to reduced costs of medications and higher clinical success rates via optimization of safer and more developable molecules showing strong efficacy for well-selected targets.</p><p>While de novo design is as-yet unproven, the success rate of the 21 AI-developed drugs that have completed Phase I trials as of December 2023 is 80%–90%, significantly higher than ~40% for traditional methods [<span>5</span>]. We continue to see an increase in the number of candidate drugs developed using AI enter clinical stages, and this trend is growing at an exponential rate—from 3 in 2016 to 17 in 2020 and 67 in 2023 [<span>5</span>].</p><p>The intersection between high-quality data access across life science modalities like imaging, multi-omics, DMRs, and very large protein repertoires, and recent advancements in the scaling and architecture of large deep learning models has led to an explosion in AI applications for healthcare. While some of this data is publicly available, much of it is proprietary and under the control of large pharmaceutical companies, partly due to regulatory and privacy concerns. Conversely, innovation in AI for drug discovery is being led by academic and industry research laboratories, often resulting in highly funded spin-off ventures like Genentech, Recursion, Absci, and more recently, Evolutionary Scale. Such AI-first life sciences companies have found success in synergistic partnerships with large pharmaceutical companies, thereby gaining access to the large proprietary datasets upon which to apply their AI expertise. Some of these partnerships have led to acquisitions such as the 2009 purchase of Genentech by Roche for approximately $46.8 billion, highlighting the value that AI internalization brings to large pharmaceutical companies.</p><p>The use of AI is poised to cover the full life cycle of a drug product, including drug discovery, drug development, and application assessment in a regulatory setting. Recent research from the Food and Drug Administration (FDA) included two distinct case studies. The first case exemplifies the use of conventional machine learning (ML) approaches through a project aimed at decoding kinase–adverse event associations for small molecule kinase inh
{"title":"AI In Action: Redefining Drug Discovery and Development","authors":"Anshul Kanakia, Mark Sale, Liang Zhao, Zhu Zhou","doi":"10.1111/cts.70149","DOIUrl":"10.1111/cts.70149","url":null,"abstract":"<p>AI has revolutionized the drug discovery space in recent years, with applications ranging from highly accurate structure predictions of proteins [<span>1</span>], to the design and optimization of both small and large molecules [<span>2</span>]. Several large foundational models have been developed for encoding functional information of proteins in a powerful way to support the drug development pipeline [<span>3, 4</span>]. Figure 1 highlights the areas in the pipeline where AI now plays a significant role and is poised to disrupt traditional experimental techniques. The culmination of AI-driven discovery is de novo design, where the entire preclinical pipeline can be performed in silico, resulting in billions of dollars of R&D cost savings, translating to reduced costs of medications and higher clinical success rates via optimization of safer and more developable molecules showing strong efficacy for well-selected targets.</p><p>While de novo design is as-yet unproven, the success rate of the 21 AI-developed drugs that have completed Phase I trials as of December 2023 is 80%–90%, significantly higher than ~40% for traditional methods [<span>5</span>]. We continue to see an increase in the number of candidate drugs developed using AI enter clinical stages, and this trend is growing at an exponential rate—from 3 in 2016 to 17 in 2020 and 67 in 2023 [<span>5</span>].</p><p>The intersection between high-quality data access across life science modalities like imaging, multi-omics, DMRs, and very large protein repertoires, and recent advancements in the scaling and architecture of large deep learning models has led to an explosion in AI applications for healthcare. While some of this data is publicly available, much of it is proprietary and under the control of large pharmaceutical companies, partly due to regulatory and privacy concerns. Conversely, innovation in AI for drug discovery is being led by academic and industry research laboratories, often resulting in highly funded spin-off ventures like Genentech, Recursion, Absci, and more recently, Evolutionary Scale. Such AI-first life sciences companies have found success in synergistic partnerships with large pharmaceutical companies, thereby gaining access to the large proprietary datasets upon which to apply their AI expertise. Some of these partnerships have led to acquisitions such as the 2009 purchase of Genentech by Roche for approximately $46.8 billion, highlighting the value that AI internalization brings to large pharmaceutical companies.</p><p>The use of AI is poised to cover the full life cycle of a drug product, including drug discovery, drug development, and application assessment in a regulatory setting. Recent research from the Food and Drug Administration (FDA) included two distinct case studies. The first case exemplifies the use of conventional machine learning (ML) approaches through a project aimed at decoding kinase–adverse event associations for small molecule kinase inh","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70149","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143257304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To plan future steps for the implementation and regulation of pharmacogenetic testing, any issue in the management of pharmacogenetic information by regulatory bodies must be identified. In this paper, an analysis of pharmacogenetic information in the summary of product characteristics (SmCPs) of drugs approved by Italian Drug Agency (AIFA) was conducted. Among 4214 SmCPs of 1063 active ingredients, 53.2% (n = 2240) included pharmacogenetic information in at least one section, most frequently for drugs in the Anatomical Therapeutic Chemical category “Antineoplastic and immunomodulatory agents”. To contextualize these data in the international scenario, a pharmacogenetic level of actionability, based on AIFA SmCPs, was assigned to 608 drug/gene pairs included in FDA's “Table of Pharmacogenomic Biomarkers in Drug Labels”, according to PharmGKB (The Pharmacogenomics Knowledge Base). Approximately 67% of drug/gene pairs were deemed classifiable: Based on SmCPs phrasing, for half of them the genetic testing was cataloged as “required” or “recommended” (mainly tumor somatic variants), whereas 40% as “actionable” (mostly PK/PD-related germline variants). The comparison with other regulatory agencies highlighted a discordance in the assigned pharmacogenetic levels of actionability ranging from 1% to 14%. This discrepancy may also point out the need to rethink the language used in AIFA-approved SmCPs to clarify whether a pharmacogenetic test is necessary or not and for which subjects it has been recommended. For the first time, a detailed evaluation and comparative analysis of the pharmacogenetic information on Italian SmCPs was presented, placing it in an international context and laying the groundwork for rethinking pharmacogenetic indications in AIFA-approved SmCPs.
{"title":"Pharmacogenetic Information on Drug Labels of the Italian Agency of Medicines (AIFA): Actionability and Comparison Across Other Regulatory Agencies","authors":"Antonino Moschella, Soumaya Mourou, Samantha Perfler, Enrico Zoroddu, Daiana Bezzini, Dorian Soru, Claudia Trignano, Monica Miozzo, Alessio Squassina, Erika Cecchin, Matteo Floris","doi":"10.1111/cts.70138","DOIUrl":"https://doi.org/10.1111/cts.70138","url":null,"abstract":"<p>To plan future steps for the implementation and regulation of pharmacogenetic testing, any issue in the management of pharmacogenetic information by regulatory bodies must be identified. In this paper, an analysis of pharmacogenetic information in the summary of product characteristics (SmCPs) of drugs approved by Italian Drug Agency (AIFA) was conducted. Among 4214 SmCPs of 1063 active ingredients, 53.2% (<i>n</i> = 2240) included pharmacogenetic information in at least one section, most frequently for drugs in the Anatomical Therapeutic Chemical category “Antineoplastic and immunomodulatory agents”. To contextualize these data in the international scenario, a pharmacogenetic level of actionability, based on AIFA SmCPs, was assigned to 608 drug/gene pairs included in FDA's “Table of Pharmacogenomic Biomarkers in Drug Labels”, according to PharmGKB (The Pharmacogenomics Knowledge Base). Approximately 67% of drug/gene pairs were deemed classifiable: Based on SmCPs phrasing, for half of them the genetic testing was cataloged as “required” or “recommended” (mainly tumor somatic variants), whereas 40% as “actionable” (mostly PK/PD-related germline variants). The comparison with other regulatory agencies highlighted a discordance in the assigned pharmacogenetic levels of actionability ranging from 1% to 14%. This discrepancy may also point out the need to rethink the language used in AIFA-approved SmCPs to clarify whether a pharmacogenetic test is necessary or not and for which subjects it has been recommended. For the first time, a detailed evaluation and comparative analysis of the pharmacogenetic information on Italian SmCPs was presented, placing it in an international context and laying the groundwork for rethinking pharmacogenetic indications in AIFA-approved SmCPs.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"18 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70138","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}