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Formulation Development of Small-Volume Parenteral Products
Published in Sandeep Nema, John D. Ludwig, Parenteral Medications, 2019
Madhav S. Kamat, Patrick P. DeLuca
Sterile preparations for parenteral use are commonly described according to the physical state of the product as follows: Liquid preparations that are drug substances or solutions thereof, for example, [DRUG] Injection.Dry solids that, upon the addition of suitable vehicles, yield solutions conforming in all respects to the requirements for Injections, for example, [DRUG] for Injection.Liquid preparations of drug substances dissolved or dispersed in a suitable emulsion medium, for example, [DRUG] Injectable Emulsion.Liquid preparations of solids suspended in a suitable liquid medium, for example, [DRUG] Injectable Suspension.Dry solids that, upon the addition of suitable vehicles, yield preparations conforming in all respects to the requirements for Injectable Suspensions, for example, [DRUG] for Injectable Suspension.
Viscoelasticity and poro-viscoelasticity
Published in Benjamin Loret, Fernando M. F. Simões, Biomechanical Aspects of Soft Tissues, 2017
Benjamin Loret, Fernando M. F. Simões
A localized fluid infusion in a viscoelastic polymer gel is considered in Netti et al. [2003]. The model aims at describing the drug delivery process in a dynamically changing mechanical environment as the drug transport affects, and is affected by, the mechanical state. In fact, while polymer gels are engineered to deliver drugs according to a specific time course, the mechanical state in vivo has to be accounted for. An application could be intratumoral drug injection, which, unlike systemic drug delivery, has not to overcome the high interstitial fluid pressure barrier. Practical issues for such a goal are that a fast infusion may lead to tissue fracture (by tensile hoop stress) around the injection point, which may compromise the homogeneous delivery of the drug in the tumor. On the other hand, cyclic mechanical compression of a polymer gel with bound macromolecules (VEGF, etc.) results in larger release than static compression, K.Y. Lee et al. [2000].
Graphene for Brain Targeting
Published in Raj K. Keservani, Anil K. Sharma, Rajesh K. Kesharwani, Nanocarriers for Brain Targeting, 2019
S. J. Owonubi, B. A. Aderibigbe, V. O. Fasiku, E. Mukwevho, E. R. Sadiku
Targeting in the passive form, sometimes called “physical targeting,” is grounded on the design of a drug-carrier complex which evades elimination from the body via excretion, phagocytosis, metabolism, and opsonization in order that the therapeutic formulation resides flowing in the blood stream, thereby allowing its spread to the defined target locale by stimuli such as pH, temperature, shape, or molecular size. Examples of passive drug targeting are direct drug injection and catheterization. Passive targeting exploits the possible alteration to the physiology of the tissues which are diseased in altered pathological situations.
Hybrid fractional-order optimal control problem for immuno-chemotherapy with gene therapy and time-delay: numerical treatments
Published in International Journal of Modelling and Simulation, 2023
M. M. Abou Hasan, S. M. AL-Mekhlafi, K. Udhayakumar, F. A. Rihan
Optimal drug control has been a focus of recent research for cancer treatments, with the goal of designing an efficient treatment protocol [15,16]. Rihan et al. [17] confirmed from their model that immuno-chemotherapy performed better than chemotherapy alone. Based on optimally controlled immunotherapeutic drug ACI, the author explored the dynamical behavior of the glioma-immune interaction in the [18]. A mathematical model combining chemotherapeutic agents with angiogenic inhibitor scheduling was proposed by D’Onofrio et al. [19]. In Leszczynski et al. [20], they examined an optimal control problem for removing a single drug from a purely mathematical model of cancer chemotherapy. By proposing an optimal model for chemotherapy treatment, the authors in [21] found the best rate of drug injection. Based on a delayed model of tumor-immune interactions, Rihan et al. [15] found the optimal immuno-chemotherapy protocol that decreased tumor load in a few months.
State/Parameter Identification in Cancerous Models Using Unscented Kalman Filter
Published in Cybernetics and Systems, 2022
Pariya Khalili, Ramin Vatankhah, Mohammad Mehdi Arefi
The last two equations are for the amount of CA and AA left in the body. The only positive terms are the drug injection rate ( and ). The negative includes the drug washout rate from the body ( and ) and the interaction of the body and cancer cells against drugs. In the above formulation, the influence of a CA on ECs is neglected. The exact values of the parameters are discussed and referenced in Pinho et al. (2013). The mathematical stability proof has also been investigated in the article by dividing the model into subsystems. For instance, it can be proved mathematically that the system model without any treatment leads to death, or that AA cannot cure the patient, and with its combination with CA, better results will be obtained (see further details in Pinho et al. (2013).
Blood pressure response simulator to vasopressor drug infusion (PressorSim)
Published in International Journal of Control, 2021
Guoyan Cao, Karolos M. Grigoriadis
The injection delay (T(t)) response to drug infusion denotes drug injection transportation. It is noted that that delay estimation follows a basic rule based on clinical experiments: during the first injection the delay inherits a large value and then decreases and reaches a steady-state value (Luspay & Grigoriadis, 2016). The rule explains the pharmacological phenomenon that in the first injection, patients’ bodies resist the vasoactive drug which causes larger delay value, while in later injections, patients’ bodies generally adapt to the vasoactive drug, thus the delays gradually reach steady-state value. In Figures 7 and 8, the estimation of delay illustrates the pharmacological phenomenon. We model the nonlinear pharmacological phenomenon as a Poisson hidden Markov model (HMM), because the pharmacological phenomenon essentially is a Markov chain, which means that the current delay state only depends on the latest previous delay state, and Poisson distribution is usually used to describe delay distribution.