搜索结果：Drug design, development and therapy

In this paper, a methodology is proposed that enables to analyze the sensitivity of the outcome of a therapy to unavoidable high dispersion of the patient specific parameters on one hand and to the choice of the parameters that define the drug delivery feedback strategy on the other hand. More precisely, a method is given that enables to extract and rank the most influent parameters that determine the probability of success/failure of a given feedback therapy for a given set of initial conditions over a cloud of realizations of uncertainties. Moreover predictors of the expectations of the amounts of drugs being used can also be derived. This enables to design an efficient stochastic optimization framework that guarantees safe contraction of the tumor while minimizing a weighted sum of the quantities of the different drugs being used. The framework is illustrated and validated using the example of a mixed therapy of cancer involving three combined drugs namely: a chemotherapy drug, an immunology vaccine and an immunotherapy drug. Finally, in this specific case, it is shown that dash-boards can be built in the 2D-space of the most influent state components that summarize the outcomes

The COVID-19 pandemic has resulted in more than 30.35 million infections and 9, 50, 625 deaths in 212 countries over the last few months. Different drug intervention acting at multiple stages of pathogenesis of COVID-19 can substantially reduce the infection induced mortality. The current within-host mathematical modeling studies deals with the optimal drug regimen and the efficacy of combined therapy in treatment of COVID-19. The drugs/interventions considered include Arbidol, Remdesivir, Inteferon (INF) and Lopinavir/Ritonavir. It is concluded that these drug interventions when administered individually or in combination reduce the infected cells and viral load. Four scenarios involving administration of single drug intervention, two drug interventions, three drug interventions and all the four have been discussed. In all these scenarios the optimal drug regimen is proposed based on two methods. In the first method these medical interventions are modeled as control interventions and a corresponding objective function and optimal control problem is formulated. In this setting the optimal drug regimen is proposed. Later using the the comparative effectiveness method the optimal dru

This research presents a mathematical model of glioma growth dynamics with drug resistance, capturing interactions among five cell populations: glial cells, sensitive glioma cells, resistant glioma cells, endothelial cells, and neuron cells, along with two therapy agent populations: chemotherapy and anti-angiogenic therapy. Glioma is a malignant tumor originating from glial cells, undergoes chemotherapy-induced mutations, leading to drug-resistant glioma cells. This not only impacts glioma cells but also normal cells. Combining chemotherapy and anti-angiogenic therapy, the model employs a Holling type II response function, considering optimal dosages for treatment optimization. Through analysis, three equilibrium are identified: two stable and one unstable equilibrium points. Numerical simulations, employing phase portraits and trajectory diagrams, illustrate the combined therapies impact on glioma cells. In summary, this concise model explores glioma dynamics and drug resistance, offering insights into the efficacy of combined therapies, crucial for optimizing glioma treatment.

The role of Artificial Intelligence (AI) is growing in every stage of drug development. Nevertheless, a major challenge in drug discovery AI remains: Drug pharmacokinetic (PK) and Drug-Target Interaction (DTI) datasets collected in different studies often exhibit limited overlap, creating data overlap sparsity. Thus, data curation becomes difficult, negatively impacting downstream research investigations in high-throughput screening, polypharmacy, and drug combination. We propose xImagand-DKI, a novel SMILES/Protein-to-Pharmacokinetic/DTI (SP2PKDTI) diffusion model capable of generating an array of PK and DTI target properties conditioned on SMILES and protein inputs that exhibit data overlap sparsity. We infuse additional molecular and genomic domain knowledge from the Gene Ontology (GO) and molecular fingerprints to further improve our model performance. We show that xImagand-DKI-generated synthetic PK data closely resemble real data univariate and bivariate distributions, and can adequately fill in gaps among PK and DTI datasets. As such, xImagand-DKI is a promising solution for data overlap sparsity and may improve performance for downstream drug discovery research tasks. Code

Deep learning methods have permeated into the research area of computer-aided drug design. The deep learning generative model and classical algorithm can be simultaneously used for three-dimensional (3D) drug design in the 3D pocket of the receptor. Here, three aspects of MolAICal are illustrated for drug design: in the first part, the MolAICal uses the genetic algorithm, Vinardo score and deep learning generative model trained by generative adversarial net (GAN) for drug design. In the second part, the deep learning generative model is trained by drug-like molecules from the drug database such as ZINC database. The MolAICal invokes the deep learning generative model and molecular docking for drug virtual screening automatically. In the third part, the useful drug tools are added for calculating the relative properties such as Pan-assay interference compounds (PAINS), Lipinski's rule of five, synthetic accessibility (SA), and so on. Besides, the structural similarity search and quantitative structure-activity relationship (QSAR), etc are also embedded for the calculations of drug properties in the MolAICal. MolAICal will constantly optimize and develop the current and new modules f

The birth of ChatGPT, a cutting-edge language model-based chatbot developed by OpenAI, ushered in a new era in AI. However, due to potential pitfalls, its role in rigorous scientific research is not clear yet. This paper vividly showcases its innovative application within the field of drug discovery. Focused specifically on developing anti-cocaine addiction drugs, the study employs GPT-4 as a virtual guide, offering strategic and methodological insights to researchers working on generative models for drug candidates. The primary objective is to generate optimal drug-like molecules with desired properties. By leveraging the capabilities of ChatGPT, the study introduces a novel approach to the drug discovery process. This symbiotic partnership between AI and researchers transforms how drug development is approached. Chatbots become facilitators, steering researchers towards innovative methodologies and productive paths for creating effective drug candidates. This research sheds light on the collaborative synergy between human expertise and AI assistance, wherein ChatGPT's cognitive abilities enhance the design and development of potential pharmaceutical solutions. This paper not only

Longer timelines and lower success rates of drug candidates limit the productivity of clinical trials in the pharmaceutical industry. Promising de novo drug design techniques help solve this by exploring a broader chemical space, efficiently generating new molecules, and providing improved therapies. However, optimizing for molecular characteristics found in approved oral drugs remains a challenge, limiting de novo usage. In this work, we propose NovoMol, a novel de novo method using recurrent neural networks to mass-generate drug molecules with high oral bioavailability, increasing clinical trial time efficiency. Molecules were optimized for desirable traits and ranked using the quantitative estimate of drug-likeness (QED). Generated molecules meeting QED's oral bioavailability threshold were used to retrain the neural network, and, after five training cycles, 76% of generated molecules passed this strict threshold and 96% passed the traditionally used Lipinski's Rule of Five. The trained model was then used to generate specific drug candidates for the cancer-related PDGFRα receptor and 44% of generated candidates had better binding affinity than the current state-of-the-art drug,

In the era of targeted therapy, there has been increasing concern about the development of oncology drugs based on the "more is better" paradigm, developed decades ago for chemotherapy. Recently, the US Food and Drug Administration (FDA) initiated Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development. To accommodate this paradigm shifting, we propose a dose-ranging approach to optimizing dose (DROID) for oncology trials with targeted drugs. DROID leverages the well-established dose-ranging study framework, which has been routinely used to develop non-oncology drugs for decades, and bridges it with established oncology dose-finding designs to optimize the dose of oncology drugs. DROID consists of two seamlessly connected stages. In the first stage, patients are sequentially enrolled and adaptively assigned to investigational doses to establish the therapeutic dose range (TDR), defined as the range of doses with acceptable toxicity and efficacy profiles, and the recommended phase 2 dose set (RP2S). In the second stage, patients are randomized to the doses in RP2S to assess the dose-response relationship and identify the optimal dose.

Despite all the advances achieved in the field of tumor-biology research, in most cases conventional therapies including chemotherapy are still the leading choices. The main disadvantage of these treatments, in addition to the low solubility of many antitumor drugs, is their lack of specificity, which explains the frequent occurrence of serious side effects due to nonspecific drug uptake by healthy cells. Progress in nanotechnology and its application in medicine have provided new opportunities and different smart systems. Such systems can improve the intracellular delivery of the drugs due to their multifunctionality and targeting potential. The purpose of this manuscript is to review and analyze the recent progress made in nanotherapy applied to cancer treatment. First, we provide a global overview of cancer and different smart nanoparticles currently used in oncology. Then, we analyze in detail the development of drug-delivery strategies in cancer therapy, focusing mainly on the intravenously administered smart nanoparticles with protein corona to avoid immune-system clearance. Finally, we discuss the challenges, clinical trials, and future directions of the nanoparticle-based t

Resistance to therapy remains a significant challenge in cancer treatment, often due to the presence of a stem-like cell population that drives tumor recurrence post-treatment. Moreover, many anticancer therapies induce plasticity, converting initially drug-sensitive cells to a more resistant state, e.g. through epigenetic processes and de-differentiation programs. Understanding the balance between therapeutic anti-tumor effects and induced resistance is critical for identifying treatment strategies. In this study, we introduce a robust statistical framework, based on multi-type branching process models of the evolutionary dynamics of tumor cell populations, to detect and quantify therapy-induced resistance phenomena from high throughput drug screening data. Through comprehensive in silico experiments, we show the efficacy of our framework in estimating parameters governing population dynamics and drug responses in a heterogeneous tumor population where cell state transitions are influenced by the drug. Finally, using recent in vitro data from multiple sources, we demonstrate that our framework is effective for analyzing real-world data and generating meaningful predictions.

Acute lymphoblastic leukemia (ALL) is a heterogeneous hematologic malignancy involving the abnormal proliferation of immature lymphocytes, accounting for most pediatric cancer cases. ALL management in children has seen great improvement in the last decades thanks to better understanding of the disease leading to improved treatment strategies evidenced through clinical trials. Commonly a first course of chemotherapy (induction phase) is administered, followed by treatment with a combination of anti-leukemia drugs. A measure of the efficacy early in the course of therapy is minimal residual disease (MRD). MRD quantifies residual tumor cells and indicates the effectiveness of the treatment over the course of therapy. MRD positivity is defined for values of MRD greater than 0.01%, yielding left-censored observations. We propose a Bayesian model to study the relationship between patient features and MRD observed at two time points during the induction phase. Specifically, we model the observed MRD values via an auto-regressive model, accounting for left-censoring of the data and for the fact that some patients are already in remission after the induction phase. Patient characteristics a

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, highlighting the urgent need for advanced therapeutic strategies. Nanoparticle-based drug delivery systems have emerged as a promising approach to improve the specificity and efficacy of anticancer treatments. This review examines three cutting-edge mesoporous silica nanoparticle (MSN)-based drug delivery to introduce novel CRC therapy, each utilizing unique functionalization strategies for targeted drug release. The first system, hyaluronidase-responsive MSN-HA/DOX, employs biotin-modified hyaluronic acid to facilitate dual-stimulus drug release in the tumor microenvironment, exhibiting enhanced in vivo tumor inhibition. The DOX/SLN-PEG-Biotin utilizes polyethylene glycol and biotin to improve drug stability and target biotin-overexpressing CRC cells, demonstrating superior anti-cancer efficacy in vitro and in vivo. Lastly, galactosylated chitosan-functionalized MSNs enable targeted delivery through asialoglycoprotein receptors, providing controlled drug release and strong therapeutic potential. Collectively, these systems highlight the advancements in nanoparticle functionalization for CRC trea

Designing new chemical compounds with desired pharmaceutical properties is a challenging task and takes years of development and testing. Still, a majority of new drugs fail to prove efficient. Recent success of deep generative modeling holds promises of generation and optimization of new molecules. In this review paper, we provide an overview of the current generative models, and describe necessary biological and chemical terminology, including molecular representations needed to understand the field of drug design and drug response. We present commonly used chemical and biological databases, and tools for generative modeling. Finally, we summarize the current state of generative modeling for drug design and drug response prediction, highlighting the state-of-art approaches and limitations the field is currently facing.

Cell heterogeneity plays an important role in patient responses to drug treatments. In many cancers, it is associated with poor treatment outcomes. Many modern drug combination therapies aim to exploit cell heterogeneity, but determining how to optimise responses from heterogeneous cell populations while accounting for multi-drug synergies remains a challenge. In this work, we introduce and analyse a general optimal control framework that can be used to model the treatment response of multiple cell populations that are treated with multiple drugs that mutually interact. In this framework, we model the effect of multiple drugs on the cell populations using a system of coupled semi-linear ordinary differential equations and derive general results for the optimal solutions. We then apply this framework to three canonical examples and discuss the wider question of how to relate mathematical optimality to clinically observable outcomes, introducing a systematic approach to propose qualitatively different classes of drug dosing inspired by optimal control.

Colorectal cancer (CRC) continues to be a significant global health burden, prompting the need for more effective and targeted therapeutic strategies. Nanoparticle-based drug delivery systems have emerged as a promising approach to address the limitations of conventional chemotherapy, offering enhanced specificity, reduced systemic toxicity, and improved therapeutic outcomes. This paper provides an in-depth review of the current advancements in the application of nanoparticles as vehicles for targeted drug delivery in CRC therapy. It covers a variety of nanoparticle types, including liposomes, polymeric nanoparticles, dendrimers, and mesoporous silica nanoparticles (MSNs), with a focus on their design, functionalization, and mechanisms of action. This review also examines the challenges associated with the clinical translation of these technologies and explores future directions, emphasizing the potential of nanoparticle-based systems to revolutionize CRC treatment.

We study a class of structured optimal control problems in which the main diagonal of the dynamic matrix is a linear function of the design variable. While such problems are in general challenging and nonconvex, for positive systems we prove convexity of the $H_2$ and $H_\infty$ optimal control formulations which allow for arbitrary convex constraints and regularization of the control input. Moreover, we establish differentiability of the $H_\infty$ norm when the graph associated with the dynamical generator is weakly connected and develop a customized algorithm for computing the optimal solution even in the absence of differentiability. We apply our results to the problems of leader selection in directed consensus networks and combination drug therapy for HIV treatment. In the context of leader selection, we address the combinatorial challenge by deriving upper and lower bounds on optimal performance. For combination drug therapy, we develop a customized subgradient method for efficient treatment of diseases whose mutation patterns are not connected.

Cancer and healthy cells have distinct distributions of molecular properties and thus respond differently to drugs. Cancer drugs ideally kill cancer cells while limiting harm to healthy cells. However, the inherent variance among cells in both cancer and healthy cell populations increases the difficulty of selective drug action. Here we propose a classification framework based on the idea that an ideal cancer drug should maximally discriminate between cancer and healthy cells. We first explore how molecular markers can be used to discriminate cancer cells from healthy cells on a single cell basis, and then how the effects of drugs are statistically predicted by these molecular markers. We then combine these two ideas to show how to optimally match drugs to tumor cells. We find that expression levels of a handful of genes suffice to discriminate well between individual cells in cancer and healthy tissue. We also find that gene expression predicts the efficacy of cancer drugs, suggesting that the cancer drugs act as classifiers using gene profiles. In agreement with our first finding, a small number of genes predict drug efficacy well. Finally, we formulate a framework that defines a

Spoken medical dialogue systems are increasingly attracting interest to enhance access to healthcare services and improve quality and traceability of patient care. In this paper, we focus on medical drug prescriptions acquired on smartphones through spoken dialogue. Such systems would facilitate the traceability of care and would free clinicians' time. However, there is a lack of speech corpora to develop such systems since most of the related corpora are in text form and in English. To facilitate the research and development of spoken medical dialogue systems, we present, to the best of our knowledge, the first spoken medical drug prescriptions corpus, named PxSLU. It contains 4 hours of transcribed and annotated dialogues of drug prescriptions in French acquired through an experiment with 55 participants experts and non-experts in prescriptions. We also present some experiments that demonstrate the interest of this corpus for the evaluation and development of medical dialogue systems.

Network science is already making an impact on the study of complex systems and offers a promising variety of tools to understand their formation and evolution (1-4) in many disparate fields from large communication networks (5,6), transportation infrastructures (7) and social communities (8,9) to biological systems (1,10,11). Even though new highthroughput technologies have rapidly been generating large amounts of genomic data, drug design has not followed the same development, and it is still complicated and expensive to develop new single-target drugs. Nevertheless, recent approaches suggest that multi-target drug design combined with a network-dependent approach and large-scale systems-oriented strategies (12-14) create a promising framework to combat complex multigenetic disorders like cancer or diabetes. Here, we investigate the human network corresponding to the interactions between all US approved drugs and human therapies, defined by known drug-therapy relationships. Our results show that the key paths in this network are shorter than three steps, indicating that distant therapies are separated by a surprisingly low number of chemical compounds. We also identify a sub-netw