Solving Limited Memory Influence Diagrams

We present a new algorithm for exactly solving decision making problems represented as influence diagrams. We do not require the usual assumptions of no forgetting and regularity; this allows us to solve problems with simultaneous decisions and limited information. The algorithm is empirically shown to outperform a state-of-the-art algorithm on randomly generated problems of up to 150 variables and 10^64 solutions. We show that these problems are NP-hard even if the underlying graph structure of the problem has low treewidth and the variables take on a bounded number of states, and that they admit no provably good approximation if variables can take on an arbitrary number of states.

Solving Limited Memory Influence Diagrams

We present a new algorithm for exactly solving decision making problems represented as influence diagrams. We do not require the usual assumptions of no forgetting and regularity; this allows us to solve problems with simultaneous decisions and limited information. The algorithm is empirically shown to outperform a state-of-the-art algorithm on randomly generated problems of up to 150 variables and 10^64 solutions. We show that the problem is NP-hard even if the underlying graph structure of the problem has small treewidth and the variables take on a bounded number of states, but that a fully polynomial time approximation scheme exists for these cases. Moreover, we show that the bound on the number of states is a necessary condition for any efficient approximation scheme.

The Complexity of Approximately Solving Influence Diagrams

Influence diagrams allow for intuitive and yet precise description of complex situations involving decision making under uncertainty. Unfortunately, most of the problems described by influence diagrams are hard to solve. In this paper we discuss the complexity of approximately solving influence diagrams. We do not assume no-forgetting or regularity, which makes the class of problems we address very broad. Remarkably, we show that when both the treewidth and the cardinality of the variables are bounded the problem admits a fully polynomial-time approximation scheme.

Probing The effects of Experimental Conditions on the Character of Drug-Polymer Phase Diagrams Constructed Using Flory-Huggins Theory

Purpose: Amorphous drug-polymer solid dispersions have been found to result in improved drug dissolution rates when compared to their crystalline counterparts. However, when the drug exists in the amorphous form it will possess a higher Gibb’s free energy than its associated crystalline state and can recrystallize. Drug-polymer phase diagrams constructed through the application of the Flory Huggins (F-H) theory contain a wealth of information regarding thermodynamic and kinetic stability of the amorphous drug-polymer system. This study was aimed to evaluate the effects of various experimental conditions on the solubility and miscibility detections of drug-polymer binary system. Methods: Felodipine (FD)-Polyvinylpyrrolidone (PVP) K15 (PVPK15) and FD-Polyvinylpyrrolidone/vinyl acetate (PVP/VA64) were the selected systems for this research. Physical mixtures with different drug loadings were mixed and ball milled. These samples were then processed using Differential Scanning Calorimetry (DSC) and measurements of melting point (Tend) and glass transition (Tg) were detected using heating rates of 0.5, 1.0 and 5.0°C/min. Results: The melting point depression data was then used to calculate the F-H interaction parameter (χ) and extrapolated to lower temperatures to complete the liquid–solid transition curves. The theoretical binodal and spinodal curves were also constructed which were used to identify regions within the phase diagram. The effects of polymer selection, DSC heating rate, time above parent polymer Tg and polymer molecular weight were investigated by identifying amorphous drug miscibility limits at pharmaceutically relevant temperatures. Conclusion: The potential implications of these findings when applied to a non-ambient processing method such as Hot Melt Extrusion (HME) are also discussed.

Using Flory-Huggins phase diagrams as a pre-formulation tool for the production of amorphous solid dispersions; a comparison between hot-melt extrusion and spray drying

Objectives: Amorphous drug forms provide a useful method of enhancing the dissolution performance of poorly water-soluble drugs; however, they are inherently unstable. In this article, we have used Flory–Huggins theory to predict drug solubility and miscibility in polymer candidates, and used this information to compare spray drying and melt extrusion as processes to manufacture solid dispersions.
Method:  Solid dispersions were characterised using a combination of thermal (thermogravimetric analysis and differential scanning calorimetry) and spectroscopic (Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction methods. 
Key Findings: Spray drying permitted generation of amorphous solid dispersions to be produced across a wider drug concentration than melt extrusion. Melt extrusion provided sufficient energy for more intimate mixing to be achieved between drug and polymer, which may improve physical stability. It was also confirmed that stronger drug–polymer interactions might be generated through melt extrusion. Remixing and dissolution of recrystallised felodipine into the polymeric matrices did occur during the modulated differential scanning calorimetry analysis, but the complementary information provided from FTIR confirms that all freshly prepared spray-dried samples were amorphous with the existence of amorphous drug domains within high drug-loaded samples. 
Conclusion: Using temperature–composition phase diagrams to probe the relevance of temperature and drug composition in specific polymer candidates facilitates polymer screening for the purpose of formulating solid dispersions.

Drug Polymer Thermodynamic Phase Diagrams in the Selection of Optimal API-Polymer Compositions for Amorphous Solid Dispersions

Purpose The aim of this work was to examine, for amorphous solid dispersions, how the thermal analysis method selected impacts on the construction of thermodynamic phase diagrams, and to assess the predictive value of such phase diagrams in the selection of optimal, physically stable API-polymer compositions. Methods Thermodynamic phase diagrams for two API/polymer systems (naproxen/HPMC AS LF and naproxen/Kollidon 17 PF) were constructed from data collected using two different thermal analysis methods. The “dynamic” method involved heating the physical mixture at a rate of 1 &[deg]C/minute. In the "static" approach, samples were held at a temperature above the polymer Tg for prolonged periods, prior to scanning at 10 &[deg]C/minute. Subsequent to construction of phase diagrams, solid dispersions consisting of API-polymer compositions representative of different zones in the phase diagrams were spray dried and characterised using DSC, pXRD, TGA, FTIR, DVS and SEM. The stability of these systems was investigated under the following conditions: 25 &[deg]C, desiccated; 25 &[deg]C, 60 % RH; 40 &[deg]C, desiccated; 40 &[deg]C, 60 % RH. Results Endset depression occurred with increasing polymer volume fraction (Figure 1a). In conjunction with this data, Flory-Huggins and Gordon-Taylor theory were applied to construct thermodynamic phase diagrams (Figure 1b). The Flory-Huggins interaction parameter (&[chi]) for naproxen and HPMC AS LF was + 0.80 and + 0.72, for the dynamic and static methods respectively. For naproxen and Kollidon 17 PF, the dynamic data resulted in an interaction parameter of - 1.1 and the isothermal data produced a value of - 2.2. For both systems, the API appeared to be less soluble in the polymer when the dynamic approach was used. Stability studies of spray dried solid dispersions could be used as a means of validating the thermodynamic phase diagrams. Conclusion The thermal analysis method used to collate data has a deterministic effect on the phase diagram produced. This effect should be considered when constructing thermodynamic phase diagrams, as they can be a useful tool in predicting the stability of amorphous solid dispersions.

Automated Equation Formulation for Causal Loop Diagrams

The annotation of Business Dynamics models with parameters and equations, to simulate the system under study and further evaluate its simulation output, typically involves a lot of manual work. In this paper we present an approach for automated equation formulation of a given Causal Loop Diagram (CLD) and a set of associated time series with the help of neural network evolution (NEvo). NEvo enables the automated retrieval of surrogate equations for each quantity in the given CLD, hence it produces a fully annotated CLD that can be used for later simulations to predict future KPI development. In the end of the paper, we provide a detailed evaluation of NEvo on a business use-case to demonstrate its single step prediction capabilities.