With the development of novel therapies such as molecularly targeted agents and immunotherapy, the maximum tolerated dose paradigm that "more is better" does not necessarily hold anymore. In this context, doses and schedules of novel therapies may be inadequately characterized and oncology drug dose-finding approaches should be revised. This is increasingly recognized by health authorities, notably through the Optimus project. We developed a Bayesian dose-finding design, called U-DESPE, which allows to either determine the optimal dosing regimen at the end of the dose-escalation phase, or use of dedicated cohorts for randomizing patients to candidate optimal dosing regimens after that safe dosing regimens have been found. U-DESPE design relies on a dose-exposure model built from pharmacokinetic data using non-linear mixed-effect modeling approaches. Then three models are built to assess the relationships between exposure and the probability of selected relevant endpoints on safety, efficacy, and pharmacodynamics. These models are then combined to predict the different endpoints for every candidate dosing regimens. Finally, a utility function is proposed to quantify the trade-off be
Herbal compounds present complex toxicity profiles that are often influenced by both intrinsic chemical properties and pharmacokinetics (PK) governing absorption and clearance. In this study, we develop a quantum regression model to predict acute toxicity severity for herbal-derived compounds by integrating toxicity data from NICEATM with pharmacological features from TCMSP.
Phase I dose-finding trials in oncology seek to find the maximum tolerated dose (MTD) of a drug under a specific schedule. Evaluating drug-schedules aims at improving treatment safety while maintaining efficacy. However, while we can reasonably assume that toxicity increases with the dose for cytotoxic drugs, the relationship between toxicity and multiple schedules remains elusive. We proposed a Bayesian dose-regimen assessment method (DRtox) using pharmacokinetics/pharmacodynamics (PK/PD) information to estimate the maximum tolerated dose-regimen (MTD-regimen), at the end of the dose-escalation stage of a trial to be recommended for the next phase. We modeled the binary toxicity via a PD endpoint and estimated the dose-regimen toxicity relationship through the integration of a dose-regimen PD model and a PD toxicity model. For the dose-regimen PD model, we considered nonlinear mixed-effects models, and for the PD toxicity model, we proposed the following two Bayesian approaches: a logistic model and a hierarchical model. We evaluated the operating characteristics of the DRtox through simulation studies under various scenarios. The results showed that our method outperforms traditi
We propose an algorithm for the efficient and robust sampling of the posterior probability distribution in Bayesian inference problems. The algorithm combines the local search capabilities of the Manifold Metropolis Adjusted Langevin transition kernels with the advantages of global exploration by a population based sampling algorithm, the Transitional Markov Chain Monte Carlo (TMCMC). The Langevin diffusion process is determined by either the Hessian or the Fisher Information of the target distribution with appropriate modifications for non positive definiteness. The present methods is shown to be superior over other population based algorithms, in sampling probability distributions for which gradients are available and is shown to handle otherwise unidentifiable models. We demonstrate the capabilities and advantages of the method in computing the posterior distribution of the parameters in a Pharmacodynamics model, for glioma growth and its drug induced inhibition, using clinical data.
Curcuma spp. extracts, particularly the dietary polyphenol curcumin, prevent colon cancer in rodents. In view of the sparse information on the pharmacodynamics and pharmacokinetics of curcumin in humans, a dose-escalation pilot study of a novel standardized Curcuma extract in proprietary capsule form was performed at doses between 440 and 2200 mg/day, containing 36-180 mg of curcumin. Fifteen patients with advanced colorectal cancer refractory to standard chemotherapies received Curcuma extract daily for up to 4 months. Activity of glutathione S-transferase and levels of a DNA adduct (M(1)G) formed by malondialdehyde, a product of lipid peroxidation and prostaglandin biosynthesis, were measured in patients' blood cells. Oral Curcuma extract was well tolerated, and dose-limiting toxicity was not observed. Neither curcumin nor its metabolites were detected in blood or urine, but curcumin was recovered from feces. Curcumin sulfate was identified in the feces of one patient. Ingestion of 440 mg of Curcuma extract for 29 days was accompanied by a 59% decrease in lymphocytic glutathione S-transferase activity. At higher dose levels, this effect was not observed. Leukocytic M(1)G levels were constant within each patient and unaffected by treatment. Radiologically stable disease was demonstrated in five patients for 2-4 months of treatment. The results suggest that (a) Curcuma extract can be administered safely to patients at doses of up to 2.2 g daily, equivalent to 180 mg of curcumin; (b) curcumin has low oral bioavailability in humans and may undergo intestinal metabolism; and (c) larger clinical trials of Curcuma extract are merited.
One challenging aspect in the clinical development of molecularly targeted therapies, which represent a new and promising approach to treating cancers, has been the identification of a biologically active dose rather than a maximum tolerated dose. The goal of the present study was to identify a pharmacokinetic/pharmacodynamic relationship in preclinical models that could be used to help guide selection of a clinical dose. SU11248, a novel small molecule receptor tyrosine kinase inhibitor with direct antitumor as well as antiangiogenic activity via targeting the vascular endothelial growth factor (VEGF), platelet-derived growth factor (PDGF), KIT, and FLT3 receptor tyrosine kinases, was used as the pharmacological agent in these studies. In mouse xenograft models, SU11248 exhibited broad and potent antitumor activity causing regression, growth arrest, or substantially reduced growth of various established xenografts derived from human or rat tumor cell lines. To predict the target SU11248 exposure required to achieve antitumor activity in mouse xenograft models, we directly measured target phosphorylation in tumor xenografts before and after SU11248 treatment and correlated this with plasma inhibitor levels. In target modulation studies in vivo, SU11248 selectively inhibited Flk-1/KDR (VEGF receptor 2) and PDGF receptor beta phosphorylation (in a time- and dose-dependent manner) when plasma concentrations of inhibitor reached or exceeded 50-100 ng/ml. Similar results were obtained in a functional assay of VEGF-induced vascular permeability in vivo. Constant inhibition of VEGFR2 and PDGF receptor beta phosphorylation was not required for efficacy; at highly efficacious doses, inhibition was sustained for 12 h of a 24-h dosing interval. The pharmacokinetic/pharmacodynamic relationship established for SU11248 in these preclinical studies has aided in the design, selection, and evaluation of dosing regimens being tested in human trials.
Efficient and robust optimization is essential for neural networks, enabling scientific machine learning models to converge rapidly to very high accuracy -- faithfully capturing complex physical behavior governed by differential equations. In this work, we present advanced optimization strategies to accelerate the convergence of physics-informed neural networks (PINNs) for challenging partial (PDEs) and ordinary differential equations (ODEs). Specifically, we provide efficient implementations of the Natural Gradient (NG) optimizer, Self-Scaling BFGS and Broyden optimizers, and demonstrate their performance on problems including the Helmholtz equation, Stokes flow, inviscid Burgers equation, Euler equations for high-speed flows, and stiff ODEs arising in pharmacokinetics and pharmacodynamics. Beyond optimizer development, we also propose new PINN-based methods for solving the inviscid Burgers and Euler equations, and compare the resulting solutions against high-order numerical methods to provide a rigorous and fair assessment. Finally, we address the challenge of scaling these quasi-Newton optimizers for batched training, enabling efficient and scalable solutions for large data-driv
The problem of maintaining the output of a positive time-invariant single-input single-output system within a predefined corridor of values is treated. For third-order plants possessing a certain structure, it is proven that the problem is always solvable under stationary conditions by means of pulse-modulated feedback. The obtained result is utilized to assess the feasibility of patient-specific pharmacokinetic-pharmacodynamic models with respect to patient safety. A population of Wiener models capturing the dynamics of a neuromuscular blockade agent is studied to investigate whether or not they can be driven into the desired output corridor by clinically acceptable sequential drug doses (boluses). It is demonstrated that low values of a parameter in the nonlinear pharmacodynamic part lie behind the detected model infeasibility.
Ligand receptor interactions are commonly assessed through equilibrium occupancy and pharmacodynamic measures that describe binding and saturation by means of bounded response curves. Thermodynamic approaches relate binding affinity to logarithmic concentration scaling, while probabilistic descriptions of occupancy arise from exponential relations. We introduce an exponential logarithmic descriptor (ELD) that integrates ligand availability and thermodynamic binding propensity within a single quantity. The logarithmic component corresponds to a thermodynamic term derived from concentration dependent free energy relations, whereas the exponential component is represented through an inverse normalized concentration term corresponding to the reciprocal of the exponential occupancy factor emerging from Boltzmann type binding formulations. We explored ELD behavior through numerical simulations spanning sub affinity, transition and saturating concentration regimes under multiple affinity conditions and time dependent exposure profiles. Compared with conventional occupancy curves, ELD retained a broader dynamic range and revealed asymmetric sensitivity across concentration scales, particul
Current closed-loop insulin delivery algorithms need to be informed of carbohydrate intake disturbances. This can be a burden on people using these systems. Pramlintide is a hormone that delays gastric emptying, which enables insulin kinetics to align with the kinetics of carbohydrate absorption. Integrating pramlintide into an automated insulin delivery system can be helpful in reducing the postprandial glucose excursion and may be helpful in enabling fully-closed loop whereby meals do not need to be announced. We present an AI-enabled dual-hormone model predictive control (MPC) algorithm that delivers insulin and pramlintide without requiring meal announcements that uses a neural network to automatically detect and deliver meal insulin. The MPC algorithm includes a new pramlintide pharmacokinetics and pharmacodynamics model that was identified using data collected from people with type 1 diabetes undergoing a meal challenge. Using a simulator, we evaluated the performance of various pramlintide delivery methods and controller models, as well as the baseline insulin-only scenario. Meals were automatically dosed using a neural network meal detection and dosing (MDD) algorithm. The
Hypoxia-activated prodrugs offer a promising strategy for targeting oxygen-deficient regions in solid tumors, which are often resistant to conventional therapies. However, modeling their behavior is challenging because of the complex interplay between oxygen availability, drug activation, and cell survival. In this work, we develop a multiscale and mixed-dimensional model that couples spatially resolved drug and oxygen transport with pharmacokinetics and pharmacodynamics to simulate the cellular response. The model integrates blood flow, oxygen diffusion and consumption, drug delivery, and metabolism. To reduce computational cost, we mitigate the global nonlinearity through a one-way coupling of the multiscale and mixed/dimensional models with a reduced 0D model for the drug metabolism. The global sensitivity analysis is then used to identify key parameters influencing drug activation and therapeutic outcome. This approach enables efficient simulation and supports the design of optimized hypoxia-targeted therapies.
Pulse-modulated feedback is utilized in drug dosing to mimic sustained over a longer period of time manual discrete dose administration, the latter is in contrast with continuous drug infusion. The intermittent mode of dosing calls for a hybrid (continuous-discrete) modeling of the closed-loop system, where the pharmacokinetics and pharmacodynamics of the drug are captured by differential equations whereas the control law is described by difference equations. Hybrid dynamics are highly nonlinear which complicates formal design of pulse-modulated feedback. This paper demonstrates complex nonlinear dynamical phenomena arising in a simple control system of dosing a neuromuscular blockade agent in anesthesia. Along with the nominal periodic regimen, undesirable nonlinear behaviors, i.e. periodic solutions of high multiplicity, multistability, as well as deterministic chaos, are shown to exist. It is concluded that design of feedback drug dosing algorithms based on a hybrid paradigm has to be informed by a thorough bifurcation analysis in order to secure patient safety.
Pramlintide's capability to delay gastric emptying has motivated its use in artificial pancreas systems, accompanying insulin as a control action. Due to the scarcity of pramlintide simulation models in the literature, in silico testing of insulin-plus-pramlintide strategies is not widely used. This work incorporates a recent pramlintide pharmacokinetics/pharmacodynamics model into the T1DM UVA/Padova simulator to adjust and validate four insulin-plus-pramlintide control algorithms. The proposals are based on an existing insulin controller and administer pramlintide either as independent boluses or as a ratio of the insulin infusion. The results of the insulin-pramlintide algorithms are compared against their insulin-alone counterparts, showing an improvement in the time in range between 3.00\% and 10.53\%, consistent with results reported in clinical trials in the literature. Future work will focus on individualizing the pramlintide model to the patients' characteristics and evaluating the implemented strategies under more challenging scenarios.
Quantitative systems pharmacology (QSP) is widely used to assess drug effects and toxicity before the drug goes to clinical trial. However, significant manual distillation of the literature is needed in order to construct a QSP model. Parameters may need to be fit, and simplifying assumptions of the model need to be made. In this work, we apply Universal Physics-Informed Neural Networks (UPINNs) to learn unknown components of various differential equations that model chemotherapy pharmacodynamics. We learn three commonly employed chemotherapeutic drug actions (log-kill, Norton-Simon, and E_max) from synthetic data. Then, we use the UPINN method to fit the parameters for several synthetic datasets simultaneously. Finally, we learn the net proliferation rate in a model of doxorubicin (a chemotherapeutic) pharmacodynamics. As these are only toy examples, we highlight the usefulness of UPINNs in learning unknown terms in pharmacodynamic and pharmacokinetic models.
Psoriasis is a long-term inflammatory skin disease that remains difficult to treat. In this study, we developed a new topical treatment by combining metal oxide nanoparticles: cerium oxide (CeO2), zinc oxide (ZnO), and silver (Ag), with natural plant extracts in a gel made from fish collagen and agar. The nanoparticles were characterized using UV-Vis spectroscopy, dynamic light scattering (DLS), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM), showing good stability and a uniform particle size distribution (ZnO averaged 66 nm). To enhance therapeutic potential, the gel was enriched with plant-derived antioxidants from bitter melon, ginger, and neem. This formulation was tested on an animal model of psoriasis. The treated group exhibited faster wound healing and reduced inflammation compared to both placebo and untreated groups, with statistically significant results (p < 0.01 to p < 0.001) observed from Day 3, becoming more pronounced by Day 14. These results indicate that the combination of nanoparticles with plant-based components in a topical gel may provide a promising new approach to psoriasis treatment. Further studies are recommen
One of the most important surgical factors is Depth of Anesthesia (DOA) control in patients. The main problem is to overcome the uncertainty and nonlinearity of the system, due to different physiological parameters of the patient's body and maintain DOA of patients in desired range during surgery. This study demonstrates a fractional order fuzzy PID controller (FOFPID) and fractional order PID controller (FOPID) to the problem. The Whale Optimization Algorithms (WOA) is used to optimized the parameters of proposed controllers. The orders of derivative and integral fractional controller is achieved by WOA. The results indicate that FOFPID has a better performance than FOPID. To check the performance of the controllers in presence of uncertainty, physiological logical model of 8 patients has been investigated. The modeling is based on Pharmacodynamic and Pharmacokinetic model. The results show the performance of the proposed method.
Functional ultrasound imaging (fUSI) is a cutting-edge technology that measures changes in cerebral blood volume (CBV) by detecting backscattered echoes from red blood cells moving within its field of view (FOV). It offers high spatiotemporal resolution and sensitivity, allowing for detailed visualization of cerebral blood flow dynamics. While fUSI has been utilized in preclinical drug development studies to explore the mechanisms of action of various drugs targeting the central nervous system, many of these studies rely on predetermined regions of interest (ROIs). This focus may overlook relevant brain activity outside these specific areas, which could influence the results. To address this limitation, we compared three machine learning approaches-convolutional neural network (CNN), support vector machine (SVM), and vision transformer (ViT)-combined with fUSI to analyze the pharmacodynamics of Dizocilpine (MK-801), a potent non-competitive NMDA receptor antagonist commonly used in preclinical models for memory and learning impairments. While all three machine learning techniques could distinguish between drug and control conditions, CNN proved particularly effective due to their a
Predicting unknown drug-drug interactions (DDIs) is crucial for improving medication safety. Previous efforts in DDI prediction have typically focused on binary classification or predicting DDI categories, with the absence of explanatory insights that could enhance trust in these predictions. In this work, we propose to generate natural language explanations for DDI predictions, enabling the model to reveal the underlying pharmacodynamics and pharmacokinetics mechanisms simultaneously as making the prediction. To do this, we have collected DDI explanations from DDInter and DrugBank and developed various models for extensive experiments and analysis. Our models can provide accurate explanations for unknown DDIs between known drugs. This paper contributes new tools to the field of DDI prediction and lays a solid foundation for further research on generating explanations for DDI predictions.
Understanding the interaction between different drugs (drug-drug interaction or DDI) is critical for ensuring patient safety and optimizing therapeutic outcomes. Existing DDI datasets primarily focus on textual information, overlooking multimodal data that reflect complex drug mechanisms. In this paper, we (1) introduce MUDI, a large-scale Multimodal biomedical dataset for Understanding pharmacodynamic Drug-drug Interactions, and (2) benchmark learning methods to study it. In brief, MUDI provides a comprehensive multimodal representation of drugs by combining pharmacological text, chemical formulas, molecular structure graphs, and images across 310,532 annotated drug pairs labeled as Synergism, Antagonism, or New Effect. Crucially, to effectively evaluate machine-learning based generalization, MUDI consists of unseen drug pairs in the test set. We evaluate benchmark models using both late fusion voting and intermediate fusion strategies. All data, annotations, evaluation scripts, and baselines are released under an open research license.
There exist excellent codes for an efficient numerical treatment of stiff and differential-algebraic problems. Let us mention {\sc Radau5} which is based on the $3$-stage Radau IIA collocation method, and its extension to problems with discrete delays {\sc Radar5}. The aim of the present work is to present a technique that permits a direct application of these codes to problems having a right-hand side with an additional distributed delay term (which is a special case of an integro-differential equation). Models with distributed delays are of increasing importance in pharmacodynamics and pharmacokinetics for the study of the interaction between drugs and the body. The main idea is to approximate the distribution kernel of the integral term by a sum of exponential functions or by a quasi-polynomial expansion, and then to transform the distributed (integral) delay term into a set of ordinary differential equations. This set is typically stiff and, for some distribution kernels (e.g., Pareto distribution), it contains discrete delay terms with constant delay. The original equations augmented by this set of ordinary differential equations can have a very large dimension, and a careful