On systems and control approaches to therapeutic gain

A systematic review of modeling and simulation approaches in designing targeted treatment technologies for Leukemia Cancer in low and middle income countries

Virtual experimentation is a widely used approach for predicting systems behaviour especially in situations where resources for physical experiments are very limited. For example, targeted treatment inside the human body is particularly challenging, and as such, modeling and simulation is utilised to aid planning before a specific treatment is administered. In such approaches, precise treatment, as it is the case in radiotherapy, is used to administer a maximum dose to the infected regions while minimizing the effect on normal tissue. Complicated cancers such as leukemia present even greater challenges due to their presentation in liquid form and not being localised in one area. As such, science has led to the development of targeted drug delivery, where the infected cells can be specifically targeted anywhere in the body. Despite the great prospects and advances of these modeling and simulation tools in the design and delivery of targeted drugs, their use by Low and Middle Income Countries (LMICs) researchers and clinicians is still very limited. This paper therefore reviews the modeling and simulation approaches for leukemia treatment using nanoparticles as an example for virtual experimentation. A systematic review from various databases was carried out for studies that involved cancer treatment approaches through modeling and simulation with emphasis to data collected from LMICs. Results indicated that whereas there is an increasing trend in the use of modeling and simulation approaches, their uptake in LMICs is still limited. According to the review data collected, there is a clear need to employ these tools as key approaches for the planning of targeted drug treatment approaches.

dNTP Supply Gene Expression Patterns after P53 Loss

Loss of the transcription factor p53 implies mRNA losses of target genes such as the p53R2 subunit of human ribonucleotide reductase (RNR). We hypothesized that other genes in the dNTP supply system would compensate for such p53R2 losses and looked for this in our own data and in data of the Gene Expression Omnibus (GEO). We found that the de novo dNTP supply system compensates for p53R2 losses with increases in RNR subunit R1, R2, or both. We also found compensatory increases in cytosolic deoxycytidine kinase (dCK) and thymidine kinase 1 (TK1) and in mitochondrial deoxyguanosine kinase (dGK), all of the salvage dNTP supply system; in contrast, the remaining mitochondrial salvage enzyme thymidine kinase 2 (TK2) decreased with p53 loss. Thus, TK2 may be more dedicated to meeting mitochondrial dNTP demands than dGK which may be more obligated to assist cytosolic dNTP supply in meeting nuclear DNA dNTP demands.

On model ensemble analyses of nonmonotonic data

Mammalian ribonucleotide reductase (RNR) activity has been reported to be nonmonotonic in ATP. If many nonlinear models are to be fitted to such data automatically as part of a model search process, use of the same initial parameter values across all models can lead to too many poor fitting, monotonic least squares fits, i.e., false model rejections. We propose that such fits can be rescued by using as initial parameter estimates the final estimates of neighboring models that do have nonmonotonic fits; here models are neighbors if complexes that they represent differ by at most one ligand. We use this approach to show that troughs in RNR activity versus ATP can be fitted similarly well by models that do or do not demand a third ATP binding site.

A mathematical model of human thymidine kinase 2 activity

The mitochondrial enzyme thymidine kinase 2 (TK2) phosphorylates deoxythymidine (dT) and deoxycytidine (dC) to form dTMP and dCMP, which in cells rapidly become the negative-feedback end-products dTTP and dCTP. TK2 kinetic activity exhibits Hill coefficients of ∼0.5 (apparent negative cooperativity) for dT and ∼1 for dC. We present a mathematical model of TK2 activity that is applicable if TK2 exists as two monomer forms in equilibrium.

Using adaptive model predictive control to customize maintenance therapy chemotherapeutic dosing for childhood acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is a common childhood cancer in which nearly one-quarter of patients experience a disease relapse. However, it has been shown that individualizing therapy for childhood ALL patients by adjusting doses based on the blood concentration of active drug metabolite could significantly improve treatment outcome. An adaptive model predictive control (MPC) strategy is presented in which maintenance therapy for childhood ALL is personalized using routine patient measurements of red blood cell mean corpuscular volume as a surrogate for the active drug metabolite concentration. A clinically relevant mathematical model is developed and used to describe the patient response to the chemotherapeutic drug 6-mercaptopurine, with some model parameters being patient-specific. During the course of treatment, the patient-specific parameters are adaptively identified using recurrent complete blood count measurements, which sufficiently constrain the patient parameter uncertainty to support customized adjustments of the drug dose. While this work represents only a first step toward a quantitative tool for clinical use, the simulated treatment results indicate that the proposed mathematical model and adaptive MPC approach could serve as valuable resources to the oncologist toward creating a personalized treatment strategy that is both safe and effective.

Automated mass action model space generation and analysis methods for two-reactant combinatorially complex equilibriums: an analysis of ATP-induced ribonucleotide reductase R1 hexamerization data

BACKGROUND

Ribonucleotide reductase is the main control point of dNTP production. It has two subunits, R1, and R2 or p53R2. R1 has 5 possible catalytic site states (empty or filled with 1 of 4 NDPs), 5 possible s-site states (empty or filled with ATP, dATP, dTTP or dGTP), 3 possible a-site states (empty or filled with ATP or dATP), perhaps two possible h-site states (empty or filled with ATP), and all of this is folded into an R1 monomer-dimer-tetramer-hexamer equilibrium where R1 j-mers can be bound by variable numbers of R2 or p53R2 dimers. Trillions of RNR complexes are possible as a result. The problem is to determine which are needed in models to explain available data. This problem is intractable for 10 reactants, but it can be solved for 2 and is here for R1 and ATP.

RESULTS

Thousands of ATP-induced R1 hexamerization models with up to three (s, a and h) ATP binding sites per R1 subunit were automatically generated via hypotheses that complete dissociation constants are infinite and/or that binary dissociation constants are equal. To limit the model space size, it was assumed that s-sites are always filled in oligomers and never filled in monomers, and to interpret model terms it was assumed that a-sites fill before h-sites. The models were fitted to published dynamic light scattering data. As the lowest Akaike Information Criterion (AIC) of the 3-parameter models was greater than the lowest of the 2-parameter models, only models with up to 3 parameters were fitted. Models with sums of squared errors less than twice the minimum were then partitioned into two groups: those that contained no occupied h-site terms (508 models) and those that contained at least one (1580 models). Normalized AIC densities of these two groups of models differed significantly in favor of models that did not include an h-site term (Kolmogorov-Smirnov p < 1 x 10(-15)); consistent with this, 28 of the top 30 models (ranked by AICs) did not include an h-site term and 28/30 > 508/2088 with p < 2 x 10(-15). Finally, 99 of the 2088 models did not have any terms with ATP/R1 ratios >1.5, but of the top 30, there were 14 such models (14/30 > 99/2088 with p < 3 x 10(-16)), i.e. the existence of R1 hexamers with >3 a-sites occupied by ATP is also not supported by this dataset.

CONCLUSION

The analysis presented suggests that three a-sites may not be occupied by ATP in R1 hexamers under the conditions of the data analyzed. If a-sites fill before h-sites, this implies that the dataset analyzed can be explained without the existence of an h-site.

Nanovehicular intracellular delivery systems

This article provides an overview of principles and barriers relevant to intracellular drug and gene transport, accumulation and retention (collectively called as drug delivery) by means of nanovehicles (NV). The aim is to deliver a cargo to a particular intracellular site, if possible, to exert a local action. Some of the principles discussed in this article apply to noncolloidal drugs that are not permeable to the plasma membrane or to the blood-brain barrier. NV are defined as a wide range of nanosized particles leading to colloidal objects which are capable of entering cells and tissues and delivering a cargo intracelullarly. Different localization and targeting means are discussed. Limited discussion on pharmacokinetics and pharmacodynamics is also presented. NVs are contrasted to micro-delivery and current nanotechnologies which are already in commercial use. Newer developments in NV technologies are outlined and future applications are stressed. We also briefly review the existing modeling tools and approaches to quantitatively describe the behavior of targeted NV within the vascular and tumor compartments, an area of particular importance. While we list "elementary" phenomena related to different level of complexity of delivery to cancer, we also stress importance of multi-scale modeling and bottom-up systems biology approach.