About

Brian Cicali, Ph.D., M.S., is an Assistant Professor in the Department of Pharmaceutics at the University of Florida College of Pharmacy. He received his B.S. in pharmacology and toxicology from the Philadelphia College of Pharmacy, his M.S. in computational science from Stockton University, and his Ph.D. in pharmaceutical sciences from the University of Florida College of Pharmacy. His research interests focus on the use of quantitative clinical pharmacology methods to optimize drug safety and efficacy, as well as the integration of real-world evidence into clinical pharmacology analyses. He has held positions at GlaxoSmithKline, Tabula Rasa HealthCare, and worked as an ORISE predoctoral fellow at the U.S. F.D.A. His work has been recognized with numerous awards, including fellowships and awards for outstanding research and publications. Dr. Cicali serves in leadership roles within several national and international organizations, such as the International Federation of Pharmaceuticals, the American College of Clinical Pharmacology, the International Society of Pharmacometrics, and the American Society of Clinical Pharmacology and Therapeutics. He is actively involved in teaching courses related to pharmacometrics, drug development, and pharmacotherapy, and leads research projects that utilize physiologically-based pharmacokinetic modeling and other quantitative methods to improve drug therapy outcomes.

Research topics

• Internal medicine
• Medicine
• Environmental health
• Immunology
• Pharmacology
• Emergency medicine
• Anesthesia
• Intensive care medicine

Selected publications

• Explaining clinically important variability in response to simvastatin treatment using a PBPK/PD approach

  The Journal of Clinical Pharmacology · 2026-02-01

  article

  Understanding exposure-response relationships is critical for the selection of an optimal drug dose that balances efficacy and safety. For simvastatin (SV), plasma concentrations may not accurately reflect target site exposure, because its pharmacologic effect is linked to intrahepatic unbound concentrations of its active form, simvastatin hydroxy acid (SVA). SVA is taken up into hepatocytes via the OATP1B1 transporter (encoded by SLCO1B1), where it is metabolized by CYP3A4. Physiological conditions such as obesity and post-Roux-en-Y gastric bypass (RYGB) surgery can alter drug disposition and enzyme activity, impacting hepatic drug exposure. This study aimed to evaluate gene-drug interaction and disease-drug interactions affecting SVA pharmacokinetics and optimize SV dosing by linking intrahepatic unbound SVA concentration to LDL-cholesterol (LDL-C) reduction using a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling approach. Simulations across doses, genotypes, and populations revealed that SLCO1B1 c.521T>C variation significantly affects plasma SVA exposure, but not hepatic SVA exposure. Obese individuals exhibited higher plasma and hepatic SVA exposure than non-obese individuals. A 20 mg dose achieved a 30-49% LDL-C reduction in obese subjects, regardless of SLCO1B1 genotype, whereas non-obese subjects may require 40 mg to achieve similar efficacy. In conclusion, systemic drug concentration or genotyping alone are insufficient to predict statin response. Instead, information on genetic and physiological variability needs to be integrated into a PBPK/PD framework to select optimal doses across diverse populations.

  PublisherDOI

• Translational Model to Predict Lung and Prostate Distribution of Levofloxacin in Humans

  Pharmaceutics · 2026-01-13

  articleOpen access

  Background/Objectives: Levofloxacin (LVX) is a fluoroquinolone approved for the treatment of bacterial pneumonia, sinusitis, and prostatitis. Emerging in vitro and preclinical evidence suggests that efflux transporters are involved in LVX’s target tissue site distribution. Methods: The objective of this research was to characterize tissue exposure using a physiologically based pharmacokinetic (PBPK) model to be able to make more educated choices for optimal doses using target site pharmacokinetics data. Results: The final PBPK model in humans was applied to simulate free target site concentrations of LVX in lung and prostate, linking to minimum inhibitory concentrations (MIC) to assess appropriateness of currently approved dosing regimens for infections in both tissues. The clinical PBPK model was able to reproduce total plasma as well as free lung and prostate exposure of LVX in humans. Efflux transporters participate in LVX distribution to prostatic but not pulmonary tissue. Our results show a good penetration of LVX in both tissues with unbound partition coefficient (Kp,uu) equal to 0.79 and 0.72 for lung and prostate, respectively. Since LVX penetration in lung and prostate is similar, different sensitivities of the pathogens to LVX will dictate the effectiveness of the approved therapeutic regimen in the treatment of bacterial pneumonia, sinusitis, and prostatitis. Conclusions: Our research provides relevant insight into LVX’s target site exposure in lung and prostate. When integrated with pathogen-specific susceptibility data, these findings can be applied to refine current dosing regimens and help optimize the pharmacological treatment outcomes.

  PublisherOA PDFDOI

• Establishing a Quantitative Framework for Combined Oral Contraceptives: Evaluating the Impact of Drug–Drug Interactions on Exposure and Clinical (Surrogate) Efficacy Endpoints

  The Journal of Clinical Pharmacology · 2025-07-22 · 1 citations

  articleOpen access

  According to the FDA Guidance for Industry on Clinical Drug Interaction (DDI) Studies with Combined Oral Contraceptives (COCs), sponsors are expected to conduct dedicated clinical DDI studies if in vitro findings suggest weak or moderate CYP3A induction, while concomitant use of COCs with strong inducers should be avoided. The guidance further suggests that a negative DDI result for drospirenone (DRSP) may be extrapolated to other progestins that are less sensitive to CYP3A modulation, such as levonorgestrel (LNG). This approach assumes that DDI-mediated changes in exposure directly translate into clinical efficacy across progestins. To evaluate the validity of this assumption, we established a quantitative link between dose, exposure, and response (Pearl Index [PI] and ovulation rate [OR]) via an integrated model-based meta-analysis, physiologically based pharmacokinetic, and pharmacokinetic/pharmacodynamic (PK/PD) modeling and simulation approach using data from 51 clinical studies in 36,040 women receiving LNG or DRSP. COCs containing LNG and DRSP were selected because they represent clinically relevant progestins at the lower and the upper end of the fraction metabolized via CYP3A4. The results of our analysis show a moderate correlation (Pearson's r = 0.52, 95% CI 0.46-0.58, P < 0.001) between PI and OR, which enables the use of OR as an ethically measurable endpoint, even at subtherapeutic doses/exposures, to predict efficacy outcomes. They further show that DDI-induced changes in exposure do not directly translate into clinical response. Therefore, DDIs with COCs should be interpreted in a PK/PD rather than a PK-only context. The quantitative framework developed in this study can serve as the scientific basis to do so.

  PublisherOA PDFDOI

• Nanotechnology: A Review on the Application of Nano-Materials in Water Purification and Nano-Medicine in the Zimbabwean Context

  SSRN Electronic Journal · 2025-01-01

  reviewOpen access
  PublisherDOI

• Glucagon-like peptide-1 receptor agonists and risk for cardiovascular events in older adults treated with levothyroxine: a target trial emulation

  Cardiovascular Diabetology · 2025-12-24 · 3 citations

  articleOpen access

  BACKGROUND: Initiation of Glucagon-Like Peptide-1 receptor agonists (GLP-1RA) in patients with type 2 diabetes (T2D) treated with levothyroxine may decrease the required levothyroxine dose due to weight loss or enhance levothyroxine absorption through delayed gastric emptying. These changes may cause thyroid hormone over-replacement and increased risk of atrial fibrillation/flutter (AF/Aflutter) and stroke. Our study aims to investigate the impact of GLP-1RA initiation on risks of AF/Aflutter and stroke in patients with T2D treated with levothyroxine, compared to sodium-glucose cotransporter 2 (SGLT2) inhibitors. METHODS: Leveraging the target trial emulation framework, we conducted a retrospective study using observational data to emulate a new user, active comparator trial examining the effects of initiating GLP-1RA (exposure group) versus SGLT2 inhibitors (control group), with random treatment assignment emulated by propensity score matching with 1:1 ratio. We used a 15% nationally representative sample of U.S. Medicare beneficiaries to identify participants > 65 years, continuously on stable dose of levothyroxine for ≥ 6 months before the index date (i.e., GLP-1RA or SGLT2 inhibitor initiation), with continuous Medicare enrollment, without malignant cancer or palliative care during 1 year before the index date. The primary outcome was AF/Aflutter, and secondary outcome was stroke, including ischemic stroke or transient ischemic attack. We assessed the per-protocol effects of GLP-1RA vs. SGLT2 inhibitors using inverse-probability-censoring weighted Cox proportional hazards models. RESULTS: After matching, the study cohort included 2,384 participants in both GLP-1RA and SGLT2 groups with mean age (SD): 73.3 (5.9) vs. 73.2 (5.8), and 71.5% and 71.8% of female. The median follow-up time was 1.05 years. Compared to SGLT2 inhibitors, initiation of GLP-1RA was associated with higher risk of AF/Aflutter (HR: 1.46; 95% CI: 1.28-1.67), while no statistically significant difference was observed between the two groups for stroke (HR: 1.17; 95% CI: 0.98-1.39). Sensitivity analyses showed consistent results, including restricting outcomes to inpatient visits, conducting an intention-to-treat analysis, applying a prevalent new user design, and substituting SGLT2 inhibitors with dipeptidyl peptidase-4 (DPP4) inhibitors as the active comparator. CONCLUSIONS: In patients with T2D historically treated with stable doses of levothyroxine, GLP-1RA initiation was associated with a higher risk of AF/Aflutter. Further research is warranted to investigate the potential roles of weight loss, TSH fluctuations, and levothyroxine dose adjustment after GLP-1RA in mediating the cardiovascular risk.

  PublisherOA PDFDOI

• Abstract 4371250: The association between GLP-1 RA initiation and cardiovascular events among patients treated with direct oral anticoagulants: A target trial emulation

  Circulation · 2025-11-03

  article

  Background: Direct oral anticoagulants (DOACs) are commonly prescribed for patients with atrial fibrillation or venous thromboembolism. Unlike Warfarin, the fixed-dose DOACs do not require close monitoring, and have minimal increase on bleeding. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) reduce weight and delay gastric emptying, witch may lead to DOAC overdosing with more adverse events. The safety of GLP-1RA initiation in patients concurrently using DOAC remains unclear. Dipeptidyl peptidase-4 (DPP4) inhibitors have minimal effect on weight and are not associated with increased bleeding risk, making them a suitable active comparator. Research Question: Is GLP-1RA initiation, compared to DPP4 inhibitors, associated with risks of stroke and bleeding in T2D patients with stable DOAC use? Methods: We emulated a prevalent new-user design trial using 15% of U.S. Medicare beneficiaries from 01/01/2011 to 12/31/2020. Patients entered the base cohort once diagnosed with T2D. Then they were randomly assigned to initiate GLP-1RA or receive DPP4 inhibitors using propensity score matching with 1:1 ratio. We included patients aged &gt;65, with stable DOAC use (defined by MPR≥80%), and continuous Medicare enrollment during one year before the index date. Outcomes include stroke and all-cause bleeding events. Patients were followed until the first occurrence of an outcome, bariatric surgery, death, Medicare disenrollment, or 12/31/2020. Cox proportional hazards models were applied. Results: After matching, 2,002 patients were included with mean follow-up of 2 years. Mean age was 74.2 vs. 76.2 years, and 51.3% vs. 45.7% were female (GLP-1RA vs. DPP4). The risks of stroke and all-cause bleeding were both comparable between GLP-1RA and DPP4 groups (stroke: 0.68 vs. 0.64/100 person-year; HR: 1.25, 95% CI: 0.57–2.75; bleeding: 4.56 vs. 6.15/100 person-year; HR: 0.79, 95% CI: 0.59–1.05). Conclusions: In T2D patients with stable DOAC use, initiation of GLP-1RA does not significantly impact the risk of stroke or bleeding. However, due to the timeframe, liraglutide was the most prescribed GLP-1RA. Studies with latest data and larger sample size are needed to further investigate the association, especially among patients received semaglutide.

  PublisherDOI

• Nanotechnology: A Review on the Application of Nano-Materials in Water Purification and Nano-Medicine in the Zimbabwean Context

  Journal of Materials Science Research and Reviews · 2025-04-22

  reviewOpen access

  International audience

  PublisherOA PDFDOI

• Enhanced Sensitivity to Tramadol in Diabetic Neuropathic Pain Compared to Nerve Compression Neuropathies: A Population <scp>PK</scp>/<scp>PD</scp> Model Analysis

  CPT Pharmacometrics & Systems Pharmacology · 2025-02-17

  articleOpen access

  ABSTRACT Neuropathic pain, often associated with diabetic neuropathy or nerve compression injuries, arises from damage or dysfunction in the somatosensory nervous system. Tramadol, frequently prescribed for this pain, has its fraction unbound and that of its active metabolite (M1) significantly altered by diabetes. Yet, dosing adjustments for diabetic neuropathic pain remain underexplored. This study developed a comprehensive population pharmacokinetics/pharmacodynamics (PK/PD) model for tramadol and its major metabolites, focusing on diabetes's impact on PK and PK‐PD relationship to identify optimal dosing regimens. Data from patients with chronic neuropathic pain on oral tramadol were used to develop enantiomer‐specific population models, considering both total and unbound concentrations. Tramadol's PK was best described by a two‐compartment model with Weibull absorption and linear elimination and a one‐compartment model with enterohepatic circulation and first‐pass metabolism for the active M1. Simulations showed higher unbound fractions of the active M1 in patients with type 1 and type 2 diabetes. Despite a 67% and 14% reduction in the AUC of total ( 1R,2R )‐M1 in patients with type 1 and type 2 diabetes, respectively, the AUC of unbound ( 1R,2R )‐M1 remained consistent. The unbound concentration of the active M1 required to achieve 50% of the maximum pain reduction (IC 50 ) was lower in patients with diabetes, indicating increased sensitivity to the drug. This model‐based approach provides valuable dosing guidance, suggesting once‐daily dosing treatments in patients with diabetes and twice‐daily dosing for patients with neuropathic pain secondary to nerve compression mechanisms.

  PublisherOA PDFDOI

• Applying Physiologically Based Pharmacokinetic Modeling to Interpret Carbamazepine’s Nonlinear Pharmacokinetics and Its Induction Potential on Cytochrome P450 3A4 and Cytochrome P450 2C9 Enzymes

  Pharmaceutics · 2024-05-30 · 6 citations

  articleOpen access

  Carbamazepine (CBZ) is commonly prescribed for epilepsy and frequently used in polypharmacy. However, concerns arise regarding its ability to induce the metabolism of other drugs, including itself, potentially leading to the undertreatment of co-administered drugs. Additionally, CBZ exhibits nonlinear pharmacokinetics (PK), but the root causes have not been fully studied. This study aims to investigate the mechanisms behind CBZ’s nonlinear PK and its induction potential on CYP3A4 and CYP2C9 enzymes. To achieve this, we developed and validated a physiologically based pharmacokinetic (PBPK) parent–metabolite model of CBZ and its active metabolite Carbamazepine-10,11-epoxide in GastroPlus®. The model was utilized for Drug–Drug Interaction (DDI) prediction with CYP3A4 and CYP2C9 victim drugs and to further explore the underlying mechanisms behind CBZ’s nonlinear PK. The model accurately recapitulated CBZ plasma PK. Good DDI performance was demonstrated by the prediction of CBZ DDIs with quinidine, dolutegravir, phenytoin, and tolbutamide; however, with midazolam, the predicted/observed DDI AUClast ratio was 0.49 (slightly outside of the two-fold range). CBZ’s nonlinear PK can be attributed to its nonlinear metabolism caused by autoinduction, as well as nonlinear absorption due to poor solubility. In further applications, the model can help understand DDI potential when CBZ serves as a CYP3A4 and CYP2C9 inducer.

  PublisherOA PDFDOI

• Recent applications of pharmacometrics and systems pharmacology approaches to improve and optimize drug therapy for pregnant and lactating women

  CPT Pharmacometrics & Systems Pharmacology · 2024-11-01 · 2 citations

  editorialOpen access

  Drug exposure to a fetus during pregnancy or an infant during breastfeeding remains a key concern for women of reproductive age, and this risk potential has led to the exclusion or under-representation of pregnant and lactating women in clinical trials. When included, studies have typically been underpowered or key biomarkers have been omitted. Ideally, robust data on drug exposure in mothers, fetuses, and breastfeeding infants are required to perform appropriate safety and efficacy assessments to make informed decisions regarding medication use in pregnant and lactating women. The US Food and Drug Administration (FDA) and the International Council of Harmonization (ICH) have recently released initiatives such as the Diversity Action Plan (DAP) (https://www.fda.gov/media/179593/download) and the E21 Efficacy Guidelines for Inclusion of Pregnant and Breastfeeding Individuals in Clinical Trials (https://database.ich.org/sites/default/files/ICH_E21_Final_Concept_Paper_2023_1106_MCApproved.pdf), which are changing the frontiers of inclusion. These regulatory initiatives are providing the impetus for the conduct of more clinical pregnancy and lactation studies by pharmaceutical companies. While the ethical, operational, enrollment, and study design challenges in study conduct are significant, they offer an opportunity for pharmacometrics and systems pharmacology (PSP) to play a key role in making clinical studies more inclusive and supporting clinical data to inform the drug label. This themed issue in CPT: Pharmacometrics and Systems Pharmacology on pregnancy and lactation offers perspectives on regulatory drivers for drug research in pregnant and lactating women, improves our understanding of non-clinical safety data to inform drug exposure in lactation, and spotlights recent quantitative applications in pharmacometrics and physiologically-based pharmacokinetic (PBPK) modeling to optimize drug therapy for pregnant and lactating women. In 2022, the FDA published the draft Diversity Plans to Improve Enrollment of Participants from Underrepresented Racial and Ethnic Populations in Clinical Trials Guidance for Industry (https://www.fda.gov/media/179593/download). While emphasizing race and ethnicity, the FDA encouraged sponsors also to submit plans for other underrepresented populations defined by pregnancy and lactation status. This year, the draft guidance was superseded by the draft Diversity Action Plans to Improve Enrollment of Participants from Underrepresented Populations in Clinical Studies, which calls to action improved enrollment of participants from underrepresented populations in clinical studies. Complementary to the FDA DAP, the ICH released the E21 final concept paper (2023) focusing on a global framework and best practices for inclusion of pregnant and lactating women in clinical trials. The ICH E21 guideline uses the ICH E11 guidance for pediatrics as its foundation. In their perspective, Coppola et al.1 offer insights into how the pediatric development extrapolation framework described in the ICH E11 guidance can be adapted to pregnancy. Using a clinical pharmacology extrapolation framework that utilizes quantitative approaches such as PBPK and population PK (Pop-PK) modeling, a pregnancy drug development strategy may be built that integrates preclinical and clinical PK, safety, efficacy, potential drug–drug interactions (DDI), post-marketing and Read-World Data (RWD). The totality of data would be used to inform decisions on the need for studies, study design and labeling updates, and new data generation to fill existing knowledge gaps. Dallman et al.2 further describe the “largely untapped potential” of model-informed drug development (MIDD) tools including quantitative systems pharmacology (QSP), PBPK, RWD, toxicology modeling, and machine learning to enhance inclusivity in clinical trials. They emphasize the importance of early engagement between regulatory agencies and pharmaceutical companies during drug development to include more pregnant individuals. Manolis et al.3 in their perspective from the European Medicines Agency (EMA) indicate that MIDD has the potential to complement clinical evidence, potentially accelerating actionable labeling information for medicine use in pregnancy and lactation as well as increasing confidence in enrolling these individuals in clinical trials. Interestingly, while the EMA recognizes the potential of PBPK as the “tool of choice,” they state that there is limited regulatory experience in this area and that “current uncertainties with PBPK in pregnancy and lactation impede their unconditional use and regulatory acceptance.” During drug development, safety data that typically inform the use of medicines during lactation (and pregnancy) fall into three categories: animal experiments, use of predictive tools to estimate fetal exposures and the partitioning of a drug into the breastmilk, and clinical safety data. Several models to predict the milk-to-plasma concentration ratio (M/P ratio) of a drug based on the physicochemical characteristics of the drug (e.g., log P, molecular weight, plasma protein binding) are available and undergoing assessment. Although some of these models have evolved to reflect the changing drug space (more metabolically stable drugs susceptible to transporter-mediated uptake and efflux), they are not yet considered sufficiently robust for prospective prediction of drug concentrations in human milk, especially if transporter-mediated secretion is involved. These predictive algorithms can be integrated within a PBPK framework to estimate and understand the transfer of drugs into breast milk as well as identify drugs that may require clinical lactation studies. The systematic review by Gong et al.4 is timely as it identifies the key mechanisms involved in the transport of drugs to breastmilk (passive transport, active transport, lipid co-transport, and transcytosis), as well as 20 transporters that are either up- or down-regulated during lactation. Similarly, the review by Sychterz et al.5 importantly examines the role of the breast cancer resistance protein (BCRP) in lactation, providing a comprehensive overview of the current evidence of its role in lactating animals and humans (mothers and infants). The authors highlight a potential noninvasive biomarker (riboflavin) for assessment of BCRP-mediated activity, and the utility of liquid biopsy to elucidate and enhance the understanding of this transporter as well as to parameterize PBPK models in lactation. The usefulness of predictive algorithms for M/P ratios within a PBPK framework for lactation is explained in detail in a tutorial by Pansari et al.6 Case studies demonstrate applications of the approach to inform and support clinical lactation studies by assessing untested scenarios (impact of colostrum, foremilk versus hindmilk, potential pH changes on the M/P ratio). Cole et al.7 used a similar approach to assess if this method is suitable to determine the exposure of the low solubility/low bioavailability drug, albendazole, and its metabolite, in breast milk. The M/P ratio was well predicted for the metabolite but not the parent drug itself, which is highly lipophilic, a finding, which has significance for other drugs with similar properties. Humerickhouse et al.8 also use a lactation modeling workflow for pregabalin, which is mainly renally excreted. The authors emphasize the need to obtain both pediatric and lactation data and integrate them into a PBPK modeling platform to further enhance our understanding of infant drug exposure through breastfeeding. Other articles in this themed issue can be broadly classified into two categories: (1) application of core/standard quantitative clinical pharmacology applications including PBPK and Pop-PK modeling and (2) newer quantitative approaches such as model-based meta analyses (MBMA) to predict drug exposures in pregnant and lactating women and potentially inform dosing. Ning et al.9 present a PBPK model that captures observed exposures of dolutegravir (UGT1A1 substrate) in non-pregnant and pregnant healthy volunteers, the umbilical cord, lactating mothers, and breastfed neonates. Interestingly, data from an ex vivo placental perfusion experiment were integrated within the feto–maternal PBPK model to parameterize the dolutegravir transplacental passage. Based on simulations, the authors provide recommendations that support the safe and effective use of dolutegravir in mothers living with HIV. Similarly, fostemsavir is a prodrug of temsavir and is approved in combination with other antiretrovirals to treat HIV infection. Without adequate treatment, HIV transmission to the fetus can occur during pregnancy, labor and birth, or through breastfeeding. Salem et al.10 present a PBPK model that demonstrates no dose adjustment is needed for fostemsavir during pregnancy. In both cases, drug exposure in the fetus, breastfed neonates, and infants in the context of prophylactic coverage or the potential to select for viral resistance was considered. Co-infection of HIV and tuberculosis is associated with poor health outcomes for mothers and infants and necessitates treatment during pregnancy. Atoyebi et al.11 leveraged a previously published PBPK model developed to elucidate the DDI between atazanavir boosted with ritonavir (ATV/r) and rifampicin, to investigate dosing strategies that can overcome the DDI effect during pregnancy considering pregnancy-induced biological changes. Pregnant women with opioid use disorder face a significant health risk. While the benefits of naltrexone as an effective treatment option have been recognized, there are a number of issues associated with the oral formulation, including extensive first-pass metabolism. Shenkoya et al.12 use clinical data from a pregnancy study involving oral naltrexone and a PBPK model for naltrexone to bridge across different routes of administration and provide dose recommendations for a newly approved extended-release injectable naltrexone to effectively manage opioid use disorder during pregnancy. Although PBPK modeling has been typically used for simulations in pregnant and lactating women, an increasing number of publications are based on pharmacometric approaches. Menshykau et al.13 use a frequentist prior approach, leveraging an existing Pop-PK model for certolizumab pegol in non-pregnant adult patients, to model the PK in pregnant women with chronic inflammatory diseases. Their analysis compared exposures between pregnant and non-pregnant women to determine whether a dose adjustment was warranted for women during pregnancy. Willeford et al.14 present a target-mediated drug disposition (TMDD) model that characterizes subcutaneously administered monoclonal antibody PK in pregnancy using time-dependent changes in body weight and central volume and drug-specific target engagement information. The authors used the model to recommend an optimal dosing regimen that maintains drug exposure above a target level for a phase II study in pregnant women. Chen et al.15 provide a novel use of MBMA to establish the dose–response relationship for combined oral contraceptives (progestins with ethinyl estradiol) with breakthrough bleeding, a pharmacodynamic end point known to contribute to non-adherence and discontinuation of combined oral contraceptives resulting in unintended pregnancies. The resulting model can be used to support optimal dosing regimens and evaluate clinically relevant factors on breakthrough bleeding. Classical examples of clinical trial designs, particularly PK studies, in pregnant and lactating women can be found where prevention of disease transmission from mother-to-child (perinatal) is a high priority. Infectious diseases, such as HIV, malaria, and tuberculosis are a leading source of perinatal disease, with high maternal infection and mortality rates, especially in low- and middle-income countries (LMICs). Examples of progress to improve clinical development strategies and practices for pregnant and lactating women in LMICs using PSP can be applied globally, irrespective of population or trial site location. This themed issue specifically sought to spotlight these examples from authors located in LMIC to demonstrate knowledge exchange and scientific perspectives on quantitative clinical pharmacology across the global community. Using an initial study design informed by stochastic simulation and estimation and with limited prior information about drug exposure in breast milk for the anti-mycobacterial drug rifampicin, Kawuma et al.16 provide a successful example of how interim analyses can be incorporated into a protocol for an observational PK study with lactating mother–infant pairs; the analysis was used to define the transfer of rifampicin to breastfed infants and quantify drug exposure in maternal plasma, breast milk, and infant plasma. Paired plasma-breast milk PK data obtained from an observational PK study were used by Ojara et al.17 to characterize drug transfer from maternal plasma to breast milk for lamivudine, an antiretroviral used to treat perinatal HIV. An infant's daily dose of lamivudine was calculated using estimated breast milk concentrations and the breast M/P ratio. The modeling framework for characterizing lactation PK can be readily extended to other drugs. Ding et al.18 characterized the PK of amodiaquine and piperaquine, artemisinin-based combination therapy used first-line for the treatment of malaria, in pregnant women across their second and third trimesters of pregnancy. Their analysis compared exposures between pregnant and non-pregnant women to determine whether a dose adjustment was warranted for women in their second and third trimesters of pregnancy. In conclusion, this themed issue on pregnancy and lactation presents recent quantitative applications of PBPK modeling and pharmacometrics in drug development, clinical, and global health settings. Interestingly, PBPK modeling, typically used as the quantitative method of choice for drug research in pregnancy, appears to have shifted application from pregnancy to lactation. An increasing number of Pop-PK modeling applications that seek to improve clinical development strategies is also encouraging. Eke et al.19 advocate for a hybrid modeling approach that combines the feto–maternal biological system parameters integrated with PBPK models with the large-scale variability captured in Pop-PK models; bridging maternal and fetal pharmacology learnings may generate more accurate predictions of drug exposure during pregnancy. Applications of more mechanistic models (e.g., QSP) and novel quantitative methods (e.g., RWD, artificial intelligence, and machine learning), all of which are essential MIDD tools, remain relatively underused presently, offering an area for advancement. Furthermore, while the primary focus of articles in this issue was small molecules, the increasing emergence of novel therapeutic modalities in drug development may warrant extensions or convergences of existing models and methods. Increasing regulation to make clinical studies more inclusive, especially through early engagement between regulators and sponsors, is likely to accelerate the application of MIDD across drug development to support and improve clinical trial design, inform dose selection, and optimize drug therapy for pregnant and lactating women. Successful engagement will facilitate equitable healthcare in maternal-fetal medicine in the years ahead; it remains the responsibility of all disciplines that participate in drug development, from clinicians to regulators to statisticians to clinical and quantitative pharmacologists. No funding was received for this work. The authors declared no competing interests for this work.

  PublisherOA PDFDOI

Frequent coauthors

• Joshua D. Brown

  Center for Drug Evaluation and Research

  26 shared

• Stephan Schmidt

  University of Florida

  16 shared

• Carl Henriksen

  12 shared

• Rodrigo Cristofoletti

  11 shared

• Amir Sarayani

  Johnson & Johnson (United States)

  10 shared

• Véronique Michaud

  McGill University

  8 shared

• Jacques Turgeon

  Galena Biopharma (United States)

  8 shared

• Karthik Lingineni

  7 shared

Awards & honors

• Distinguished Research Fellowship from Stockton University (…
• Dr. Allen J. Speigel Endowed Graduate Fellowship (2019)
• Robert and Stephany Ruffolo Graduate Education and Research…
• CPT:PSP award for most outstanding paper of 2019
• 2020 Most Informative Scientific Report from Simcyp academic…