Alfred A. Jacobs

Pharmacodynamic population analysis in chronic renal failure using artificial neural networks: a comparative study

This work presents a pharmacodynamic population analysis in chronic renal failure patients using Artificial Neural Networks (ANNs). In pursuit of an effective and cost-efficient strategy for drug delivery in patients with renal failure, two different types of ANN are applied to perform drug dose-effect modeling and their performance compared. Applied in a clinical environment, such models will allow for prediction of patient response to the drug at the effect site and, subsequently, for adjusting the dosing regimen.

Polymorphonuclear leukocyte function during hemodialysis: relationship to complement activation

Phagocytosis, H2O2 production, and C3bi receptor (CR3) expression by polymorphonuclear leukocytes (PMN) obtained from patients before, during, and after a hemodialysis treatment were evaluated by flow microfluorometry. The results were compared to changes in plasma levels of C3ades Arg and C5ades Arg. Prior to hemodialysis C3ades Arg and C5ades Arg levels, CR3 expression and phagocytosis were not different from normal controls. However, both basal and phagocytosis-induced H2O2 production were increased. C3ades Arg and C5ades Arg were increased after 15 min of dialysis; this was accompanied by transient but significant reductions in PMN count and phagocytosis and increased CR3 expression. No changes in basal or stimulated H2O2 production were observed. We conclude that PMN of hemodialysis patients are primed for an enhanced respiratory burst before dialysis is initiated. Dialysis-induced complement activation after the initiation of dialysis does not further stimulate H2O2 production or enhance the response to phagocytosis. However, complement activation may cause leukopenia and CR3 expression.

Randomized Trial of Model Predictive Control for Improved Anemia Management

BACKGROUND AND OBJECTIVES Variable hemoglobin (Hb) response to erythropoiesis stimulating agents may result in adverse outcomes. The utility of model predictive control for drug dosing was previously demonstrated. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS This was a double-blinded, randomized, controlled trial to test model predictive control for dosing erythropoietin in ESRD patients. The trial included 60 hemodialysis patients who were randomized into a treatment arm (30 subjects) that received erythropoietin doses on the basis of the computer recommendations or a control arm (30 subjects) that received erythropoietin doses on the basis of recommendations from a standard anemia management protocol (control). The subjects were followed for 8 months, and the proportions of measured Hb within the target of 11 to 12 g/dl and outside 9 to 13 g/dl were measured. Variability of the Hb level was measured by the absolute difference between the achieved Hb and the target Hb of 11.5 g/dl as well as the area under the Hb curve. RESULTS Model predictive control resulted in 15 observations >13 or <9 g/dl (outliers), a mean absolute difference between achieved Hb and 11.5 g/dl of 0.98 +/- 0.08 g/dl, and an area under the Hb curve of 2.86 +/- 1.46. The control group algorithm resulted in 30 Hb outliers (P = 0.051), produced a mean absolute difference between achieved Hb and 11.5 g/dl of 1.18 +/- 0.18 g/dl (P < 0.001 difference in variance), and an area under the Hb curve of 3.38 +/- 2.69 (P = 0.025 difference in variance). CONCLUSIONS Model predictive control of erythropoietin administration improves anemia management.

TNF-alpha stimulates increased plasma membrane guanine nucleotide binding protein activity in polymorphonuclear leukocytes.

TNF‐α enhances the response of polymorphonuclear leukocytes (PMN) to chemoattractants: however, the mechanism by which this occurs is unclear. We addressed the hypothesis that TNF‐α enhances the PMN response to chemoattractants by increasing chemoattractant receptor transmembrane signaling, using fMLP as the model chemoattractant. fMLP‐stimulated guanine nucleotide binding (G) protein activation was significantly increased in plasma membranes isolated from PMNs exposed to TNF‐α 100 U/ml for 10 minutes (TNF‐M), compared to membranes from control cells (CM). Formyl peptide receptor number and affinity were not significantly different in CM and TNF‐M. Gi and G3 content were increased in TNF‐M as measured by pertussis toxin and cholera toxin (CT) catalyzed ADP‐ribosylation, respectively. The increased Gi was coupled to the formyl peptide receptor as shown by receptor‐specific CT labeling of Gi. Immunoblot analysis showed that both Gαi2 and Gα3 were increased in TNF‐M. The functional activity of the increased G protein content was demonstrated by increased NaF‐stimulated phospholipase D activity in TNF‐α‐treated PMNs. We conclude that TNF‐α rapidly stimulates increased PMN plasma membrane expression of G proteins that couple formyl peptide receptors to effector enzymes. Regulation of G protein expression may be a significant mechanism by which TNF regulates PMN function. J. Leukoc. Biol. 57: 500–506; 1995.

Individualized Anemia Management Reduces Hemoglobin Variability in Hemodialysis Patients

One-size-fits-all protocol-based approaches to anemia management with erythropoiesis-stimulating agents (ESAs) may result in undesired patterns of hemoglobin variability. In this single-center, double-blind, randomized controlled trial, we tested the hypothesis that individualized dosing of ESA improves hemoglobin variability over a standard population-based approach. We enrolled 62 hemodialysis patients and followed them over a 12-month period. Patients were randomly assigned to receive ESA doses guided by the Smart Anemia Manager algorithm (treatment) or by a standard protocol (control). Dose recommendations, performed on a monthly basis, were validated by an expert physician anemia manager. The primary outcome was the percentage of hemoglobin concentrations between 10 and 12 g/dl over the follow-up period. A total of 258 of 356 (72.5%) hemoglobin concentrations were between 10 and 12 g/dl in the treatment group, compared with 208 of 336 (61.9%) in the control group; 42 (11.8%) hemoglobin concentrations were <10 g/dl in the treatment group compared with 88 (24.7%) in the control group; and 56 (15.7%) hemoglobin concentrations were >12 g/dl in the treatment group compared with 46 (13.4%) in the control group. The median ESA dosage per patient was 2000 IU/wk in both groups. Five participants received 6 transfusions (21 U) in the treatment group, compared with 8 participants and 13 transfusions (31 U) in the control group. These results suggest that individualized ESA dosing decreases total hemoglobin variability compared with a population protocol-based approach. As hemoglobin levels are declining in hemodialysis patients, decreasing hemoglobin variability may help reduce the risk of transfusions in this population.

Determining Optimum Hemoglobin Sampling for Anemia Management from Every-Treatment Data

BACKGROUND AND OBJECTIVES Anemia management protocols in ESRD call for hemoglobin (Hb) monitoring every 2 to 4 weeks. Short-term Hb variability affects the reliability of Hb measurement and may lead to incorrect dosing of erythropoiesis stimulating agents. We prospectively analyzed short-term Hb variability and quantified the relationship between frequency of Hb monitoring and error in Hb estimation. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS Using the Crit-Line III TQA device, we prospectively observed Hb during each dialysis treatment in 49 ESRD patients and quantified long- and short-term Hb variability. We estimated Hb from data sampled at regular intervals; 8×, 4×, 2×, or 1× per month to establish how well we account for short-term variability at different monitoring intervals. We calculated the Hb estimation error (Hb(err)) as a root mean-squared difference between the observed and estimated Hb and compared it with the measurement error. RESULTS The most accurate Hb estimation is achieved when monitoring 8× per month (Hb(err) = 0.23 ± 0.05 g/dl), but it exceeds the accuracy of the measurement device. The estimation error increases to 0.34 ± 0.07 g/dl when monitoring 4× per month, 0.39 ± 0.08 g/dl when monitoring 2× a month, and 0.45 ± 0.09 g/dl when monitoring 1× per month. Estimation error comparable to instrument error information is as follows: 8× per month, 15 patients; 4× per month, 22 patients; 2× per month, 6 patients; 1× per a month, 6 patients. CONCLUSIONS Four times a month is the clinically optimal Hb monitoring frequency for anemia management.

Chemoattractant receptor-specific differences in G protein activation rates regulate effector enzyme and functional responses.

The hypothesis that disparate neutrophil functional responses to various chemoattractants are regulated by receptor‐specific rates of G protein activation was examined in HL‐60 granulocytes. The initial rates of G protein activation and the affinity of receptor‐stimulated G proteins for GTPγS in HL‐60 membranes stimulated by fMet‐Leu‐Phe, C5a, and leukotriene B4 (LTB4) differed significantly among the chemoattractants, with a rank order of fMet‐Leu‐Phe > C5a > LTB4. Equilibrium GTPγS binding showed that all three chemoattractants activated a common pool of G proteins. Stimulation of phospholipasc D activation, measured as phosphatidylethanol generation, and superoxide release in intact cells also occurred with a rank order of fMet‐Leu‐Phe > C5a > LTB+. On the other hand, the rank order of reccptor affinities for ligand and of the EC50 of chemoattractant stimulation of GTPγS binding was C5a > LTB4 > fMet‐Leu‐Phe. C5a and LTB4 receptor densities were similar but were less than formyl peptide receptor density. Graded pertussis toxin treatment proportionally reduced superoxide release and phospholipase D activation to all three chemoattractants. The results suggest that receptor‐specific differences in G pro tein affinity for guanine nucleotides lead to different rates of guanine nucleotide exchange and, thereby, contribute to disparate effector enzyme and functional responses. J. Leukoc. Biol 57: 679–686; 1995.

Model Predictive Control with Reinforcement Learning for Drug Delivery in Renal Anemia Management

Treatment of chronic conditions often creates the challenge of an adequate drug administration. The intra- and inter-individual variability of drug response requires periodic adjustments of the dosing protocols. We describe a method, combining model predictive control for simulation of patient response and reinforcement learning for estimation of dosing strategy, to facilitate the management of anemia due to kidney failure

Fuzzy rule-based approach to automatic drug dosing in renal failure

The paper presents a fuzzy logic-based approach to automation of drug delivery in renal patients. Dialysis data collected from 200 patients serve as a basis for reconstruction of the dosing regimen in terms of rules. The fuzzy rule-based representation allows the physician for quick verification of the dosing regimen, as well as easy implementation of numerical techniques for its adjustment.

Reinforcement learning approach to individualization of chronic pharmacotherapy

Effective pharmacological therapy in chronic treatments poses many challenges to physicians. Individual response to treatment varies across patient populations. Furthermore, due to the prolonged character of the therapy, the response may change over time. A reinforcement learning-based framework is proposed for treatment individualization in the management of renal anemia. The approach is based on numerical simulation of the patient performed by Takagi-Sugeno fuzzy model and a radial basis function network implementation of an on-policy Q-learning critic. Simulation results demonstrate the potential of the proposed method to yield policies that achieve the therapeutic goal in individuals with different response characteristics.