Quantitative pharmacophore models with inductive logic programming

Three-dimensional models, or pharmacophores, describing Euclidean constraints on the location on small molecules of functional groups (like hydrophobic groups, hydrogen acceptors and donors, etc.), are often used in drug design to describe the medicinal activity of potential drugs (or ‘ligands’). This medicinal activity is produced by interaction of the functional groups on the ligand with a binding site on a target protein. In identifying structure-activity relations of this kind there are three principal issues: (1) It is often difficult to “align” the ligands in order to identify common structural properties that may be responsible for activity; (2) Ligands in solution can adopt different shapes (or `conformations’) arising from torsional rotations about bonds. The 3-D molecular substructure is typically sought on one or more low-energy conformers; and (3) Pharmacophore models must, ideally, predict medicinal activity on some quantitative scale. It has been shown that the logical representation adopted by Inductive Logic Programming (ILP) naturally resolves many of the difficulties associated with the alignment and multi-conformation issues. However, the predictions of models constructed by ILP have hitherto only been nominal, predicting medicinal activity to be present or absent. In this paper, we investigate the construction of two kinds of quantitative pharmacophoric models with ILP: (a) Models that predict the probability that a ligand is “active”; and (b) Models that predict the actual medicinal activity of a ligand. Quantitative predictions are obtained by the utilising the following statistical procedures as background knowledge: logistic regression and naive Bayes, for probability prediction; linear and kernel regression, for activity prediction. The multi-conformation issue and, more generally, the relational representation used by ILP results in some special difficulties in the use of any statistical procedure. We present the principal issues and some solutions. Specifically, using data on the inhibition of the protease Thermolysin, we demonstrate that it is possible for an ILP program to construct good quantitative structure-activity models. We also comment on the relationship of this work to other recent developments in statistical relational learning. Editors: Tamás Horváth and Akihiro Yamamoto     

D-AKAP2, a novel protein kinase A anchoring protein with a putative RGS domain

Subcellular localization directed by specific A kinase anchoring proteins (AKAPs) is a mechanism for compartmentalization of cAMP-dependent protein kinase (PKA). Using a two-hybrid screen, a novel AKAP was isolated. Because it interacts with both the type I and type II regulatory subunits, it was defined as a dual specific AKAP or D-AKAP1. Here we report the cloning and characterization of another novel cDNA isolated from that screen. This new member of the D-AKAP family, D-AKAP2, also binds both types of regulatory subunits. A message of 5 kb pairs was detected for D-AKAP2 in all embryonic stages and in all adult tissues tested. In brain, skeletal muscle, kidney, and testis, a 10-kb mRNA was identified. In testis, several small mRNAs were observed. Therefore, D-AKAP2 represents a novel family of proteins. cDNA cloning from a mouse testis library identified the full length D-AKAP2. It is composed of 372 amino acids which includes the R binding fragment, residues 333-372, at its C-terminus. Based on coprecipitation assays, the R binding domain interacts with the N-terminal dimerization domain of RIalpha and RIIalpha. A putative RGS domain was identified near the N-terminal region of D-AKAP2. The presence of this domain raises the intriguing possibility that D-AKAP2 may interact with a Galpha protein thus providing a link between the signaling machinery at the plasma membrane and the downstream kinase.     

Infections in patients with cystic fibrosis following lung transplantation

BACKGROUND: There is controversy over whether colonization with drug-resistant organisms is a contraindication to lung transplantation. METHODS: We undertook a retrospective review of the results of lung transplantation for patients with cystic fibrosis (CF) at Duke University Medical Center. RESULTS: As of May 1996, 21 patients with CF underwent bilateral lung transplantation. The first patient died within 24 h of transplantation from sepsis due to Stenotrophomonas maltophilia. Of the remaining 20 patients, 17 (85%) are alive and in stable condition. The three deaths were related primarily to bronchiolitis obliterans at 4 and 18 months in two patients and to cytomegalovirus pneumonitis at 5 months in the other patient. The 17 surviving patients have been followed up for a mean of 13 months (range, 0.5 to 34 months). Most of them were colonized and infected with multidrug-resistant organisms before transplantation. Following transplantation, 11 patients had complications from infections. One patient had bacteremia due to a panresistant Burkholderia cepacia and was treated successfully. Two patients had bacteremia and wound infection due to Burkholderia gladioli, previously thought to be pathogenic only in plants. Both patients were treated successfully. Of the six patients with Aspergillus fumigatus isolated from cultures before transplantation, only one had invasive disease following transplantation and responded to treatment. CONCLUSION: The organisms present before transplantation were not the primary cause of mortality in our patient population. Our findings suggest that lung transplantation should be considered in CF patients infected with multidrug-resistant organisms.     

A new method to estimate parameters of linear compartmental models using artificial neural networks

At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models.     

Effect of race on outcome after kidney and kidney-pancreas transplantation in type 1 diabetic patients

OBJECTIVE: The racial impact on graft outcome is not well defined in diabetic recipients. The purpose of this study is to analyze our experience with kidney-alone (A) and kidney-pancreas (KP) transplantation in type 1 diabetic recipients and evaluate the impact of racial disparity on outcome. RESEARCH DESIGN AND METHODS: The records of 217 kidney transplants (118 KA, 99 KP) performed on type 1 diabetic patients between 1985 and 1995 at the Medical University of South Carolina and the University of Texas Medical Branch were reviewed. RESULTS: A total of 53 (31%) white patients and 15 (33%) black patients experienced at least one episode of biopsy-proven acute rejection of the renal graft (NS). Patient survival at 1, 2, and 5 years was similar in white (92, 87, 69%) and black (91, 91, 69%) patients (NS). Kidney graft survival at 1, 2, and 5 years in the KA group was 72, 62, and 42% in blacks, compared with 79, 76, and 53% in whites (NS). Kidney graft survival at 1, 2, and 5 years in the KP group was 92, 92, and 74% in blacks, compared with 83, 77, and 58% in whites (NS). Pancreas graft survival at 1, 2, and 5 years was 81, 81, and 81% in blacks, compared with 81, 75, and 62% in whites (NS). Cox regression analysis revealed that donor age > or = 40 years increased the risk of renal graft failure 6.2-fold (P = 0.0001), whereas the addition of a pancreas transplant to a kidney and a living-related transplant decreased the risk of failure of the kidney graft 0.2 (P = 0.005) and 0.1 times (P = 0.005). CONCLUSIONS: Our results suggest that when compared with whites, there may be a trend toward an improved kidney and pancreas graft outcome in blacks undergoing KP transplants. These findings suggest that diabetes may override the risk factors that account for the pronounced disparity in outcome observed between nondiabetic white and black recipients.     

Stability of proximal femoral grafts in canine hip arthroplasty

In a canine model, the fixation stability of a prosthesis and proximal bone graft composite were measured relative to the distal femur. One group had the prosthesis graft composite cemented into the distal femur. The second group had the prosthesis graft composite press fit into the distal femur for biologic ingrowth. Displacements of the proximal femoral grafts relative to the host bone in each group were measured after ex vivo (acute with graft) implantation and 4 months after implantation. A third group with no osteotomy (acute intact) simulated perfect graft to host bone union. Relative displacements representing 6 degrees freedom (translation and rotation) were calculated from the displacement values measured by 9 eddy current transducers. Measurements of displacement were used to test the hypothesis that distal press fit fixation equals distal cement fixation at 4 months after implantation. In all cases the measured translations and rotations of the graft to implant construct were small and of a magnitude that should encourage bone ingrowth (< 0.05 mm and < 0.1 degree, respectively). The stability of the press fit group at 4 months was not significantly different from the cemented group in axial and transverse displacement during axial and transverse loading, respectively. There was no difference in stabilities at 4 months between distal press fit and cemented fixation in hip replacements requiring a proximal femoral graft.