Analytical Portfolio Research Articles

A bird’s eye view on the use of whole exome sequencing in rare congenital ophthalmic diseases

Phenotypic and genotypic heterogeneity in congenital ocular diseases, especially in anterior segment dysgenesis (ASD), have created challenges for proper diagnosis and classification of diseases. Over the last decade, genomic research has indeed boosted our understanding in the molecular basis of ASD and genes associated with both autosomal dominant and recessive patterns of inheritance have been described with a wide range of expressivity. Here we describe the molecular characterization of a cohort of 162 patients displaying isolated or syndromic congenital ocular dysgenesis. Samples were analyzed with diverse techniques, such as direct sequencing, multiplex ligation-dependent probe amplification, and whole exome sequencing (WES), over 20 years. Our data reiterate the notion that PAX6 alterations are primarily associated with ASD, mostly aniridia, since the majority of the cohort (66.7%) has a pathogenic or likely pathogenic variant in the PAX6 locus. Unexpectedly, a high fraction of positive samples (20.3%) displayed deletions involving the 11p13 locus, either partially/totally involving PAX6 coding region or abolishing its critical regulatory region, underlying its significance. Most importantly, the use of WES has allowed us to both assess variants in known ASD genes (i.e., CYP1B1, ITPR1, MAB21L1, PXDN, and PITX2) and to identify rarer phenotypes (i.e., MIDAS, oculogastrointestinal-neurodevelopmental syndrome and Jacobsen syndrome). Our data clearly suggest that WES allows expanding the analytical portfolio of ocular dysgenesis, both isolated and syndromic, and that is pivotal for the differential diagnosis of those conditions in which there may be phenotypic overlaps and in general in ASD.

In vitro dissolution/permeation tools for amorphous solid dispersions bioavailability forecasting I: Experimental design for PermeaLoop™

Along with the increasing demand for candidate-enabling formulations comes the need for appropriate in vitro bioavailability forecasting. Dissolution/permeation (D/P) systems employing cell-free permeation barriers are increasingly gaining interest, due to their low cost and easy application as passive diffusion bio-predictive profiling in drug product development, as this accounts for nearly 75% of new chemical entities (NCEs) absorption mechanism.To this end, this study comprises theoretical considerations on the design and experimental work towards the establishment and optimization of a PermeaLoop™ based dissolution/permeation assay to simultaneously evaluate the drug release and permeation using Itraconazole (ITZ)-based amorphous solid dispersions (ASD) formulations, with different drug loads, based on a solvent-shift approach. Alternative method conditions were tested such as: donor medium, acceptor medium and permeation barrier were screened using both PermeaPad® and PermeaPlain® 96-well plates. A range of solubilizers, namely Sodium Dodecyl Sulfate, Vitamin E-TPGS and hydroxypropyl-β-cyclodextrin, were screened as possible solubilizing additives to the acceptor medium, while donor medium was varied between blank FaSSIF (phosphate buffer) and FaSSIF. The method optimization also included the ITZ dose selection, being the ITZ single dose (100 mg) considered the most adequate to be used in further experiments to allow the comparison with in vivo studies.In the end, a standardized approach that may be applied to predict the bioavailability of weakly basic poorly soluble drug-based formulations is described, contributing to strengthening the analytical portfolio of in vitro pre-clinical drug product development.

Where is laboratory medicine headed in the next decade? Partnership model for efficient integration and adoption of artificial intelligence into medical laboratories.

The field of artificial intelligence (AI) has grown in the past 10 years. Despite the crucial role of laboratory diagnostics in clinical decision-making, we found that the majority of AI studies focus on surgery, radiology, and oncology, and there is little attention given to AI integration into laboratory medicine. We dedicated a session at the 3rd annual European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) strategic conference in 2022 to the topic of AI in the laboratory of the future. The speakers collaborated on generating a concise summary of the content that is presented in this paper. The five key messages are (1) Laboratory specialists and technicians will continue to improve the analytical portfolio, diagnostic quality and laboratory turnaround times; (2) The modularized nature of laboratory processes is amenable to AI solutions; (3) Laboratory sub-specialization continues and from test selection to interpretation, tasks increase in complexity; (4) Expertise in AI implementation and partnerships with industry will emerge as a professional competency and require novel educational strategies for broad implementation; and (5) regulatory frameworks and guidances have to be adopted to new computational paradigms. In summary, the speakers opine that the ability to convert the value-proposition of AI in the laboratory will rely heavily on hands-on expertise and well designed quality improvement initiative from within laboratory for improved patient care.

Minimizing the Risk of Missing Critical Sample Information by Using Two-Dimensional Liquid Chromatography

Analytical requirements in the biopharmaceutical, pharmaceutical, and food industries, among several others, are more demanding than ever. Chromatographic techniques are great tools to acquire detailed information on a vast number of molecules and sample types. The present challenge in research and development (R&D), as well as in quality control (QC) laboratories, is to collect as much sample information as possible. However, even with the current one-dimensional (1D) analytical portfolio, it is not possible to fully ensure that all the relevant information from a sample has been captured. This article illustrates the power of an online two-dimensional liquid chromatographic (2D-LC) setup to unravel the complexity of biopharmaceutical and pharmaceutical samples. This technology tremendously increases the resolving power in all areas where LC is applied and drastically reduces the risk of missing information about the sample.

Analytic solution to the portfolio optimization problem in a mean-variance-skewness model

ABSTRACTIn portfolio theory, it is well-known that the distributions of stock returns are often unimodal asymmetric distributions. Therefore, many researches have suggested considering the skew-normal distribution as an adequate model in quantitative finance. Such asymmetry explains why the celebrated mean-variance theory, which does not account to the skewness of distribution of returns, frequently fails to provide an optimal portfolio selection rule. In this paper, we provide a novel approach for solving the problem of optimal portfolio selection for asymmetric distributions of the stock returns, by putting it into a framework of a mean-variance-skewness measure. Moreover, our optimal solutions are explicit and are closed-form. In particular, we provide an analytical portfolio optimization solution to the exponential utility of the well-known skew-normal distribution. Our analytical solution can be investigated in comparison to other portfolio selection rules, such as the standard mean-variance model. The new methodology is illustrated numerically.

An Analytical Portfolio Credit Risk Model Based on the Extended Binomial Distribution

The binomial distribution describes the probability of the number of successes for a fixed number of identical independent experiments, each with binary out-put. In real life, practical applications like portfolio credit risk management trials are not identical and have different realization probabilities. In addition to the number, the quantitative impacts of the respective outputs are also important. There exist no complete model-side implementations for the expansion of the binomial distribution, especially not in the case of specific quantitative parameters up to now. Here, a solution of this issue is described by the extended binomial distribution. The key for solving the problem lies in the use of bijection between the elementary events of the binomial distribution and the digit sequences of binary numbers. Based on the extended binomial distribution, an analytical portfolio credit risk model is described. The binomial distribution approach minimizes the approximation error in modeling. In particular, the edges of the loss distribution can be determined in a realistic manner. This analytical portfolio credit risk model is especially predestined for management of risk concentrations and tail risks.

Time-Consistent Portfolio Policy for Asset-Liability Mean-Variance Model with State-Dependent Risk Aversion

In reality, when facing a multi-period asset-liability portfolio selection problem, the risk aversion attitude of a mean-variance investor may depend on the wealth level and liability level. Thus, in this paper, we propose a state-dependent risk aversion model for the investor, in which risk aversion is a linear function of current wealth level and current liability level. Due to the time inconsistency of the resulting multi-period asset-liability mean-variance model, we investigate its time-consistent portfolio policy by solving a nested mean-variance game formulation. We derive the analytical time-consistent portfolio policy, which takes a linear form of current wealth level and current liability level. We also analyze the influence of the risk aversion coefficients on the time-consistent portfolio policy and the investment performance via a numerical example.

Eurofins pushes into drug contract services

The testing services giant Eurofins Scientific has agreed to acquire Advinus, an India-based contract research firm owned by the conglomerate Tata Industries. The deal will mark Eurofins’s third acquisition of a contract development and manufacturing organization (CDMO) in two months. “We are confident that with access to Eurofins’s global network and technical competencies, Advinus will be able to rapidly expand its analytical portfolio and services,” says KRS Jamwal, executive director of Tata and chairperson of Advinus. The Indian firm has more than 10 years of experience supplying research services to the pharmaceutical, agricultural chemical, and cosmetics industries. And Eurofins will add an Indian element to a recent push into the pharmaceutical research services business. In June, the company announced the purchase of Alphora, an Ontario-based firm that manufactures niche small-molecule active pharmaceutical ingredients (APIs), including cytotoxic and highly potent compounds. Eurofins framed that ...

Dynamic Mean-LPM and Mean-CVaR Portfolio Optimization in Continuous-Time

Instead of controlling "symmetric" risks measured by central moments of investment return or terminal wealth, more and more portfolio models have shifted their focus to manage "asymmetric" downside risks that the investment return is below certain threshold. Among the existing downside risk measures, the lower-partial moments (LPM) and conditional value-at-risk (CVaR) are probably most promising. In this paper we investigate the dynamic mean-LPM and mean-CVaR portfolio optimization problems in continuous-time, while the current literature has only witnessed their static versions. Our contributions are two-fold, in both building up tractable formulations and deriving corresponding analytical solutions. By imposing a limit funding level on the terminal wealth, we conquer the ill-posedness exhibited in the class of mean-downside risk portfolio models. The limit funding level not only enables us to solve both dynamic mean-LPM and mean-CVaR portfolio optimization problems, but also offers a flexibility to tame the aggressiveness of the portfolio policies generated from such mean - downside risk models. More specifically, for a general market setting, we prove the existence and uniqueness of the Lagrangian multiplies, which is a key step in applying the martingale approach, and establish a theoretical foundation for developing efficient numerical solution approaches. Moreover, for situations where the opportunity set of the market setting is deterministic, we derive analytical portfolio policies for both dynamic mean-LPM and mean-CVaR formulations.

Spectroscopic tools for an automated suspension cell culture screening system

Background We developed an automated, multiwell plate (MWP) based screening system for suspension cell cultures which is now routinely used in cell culture process development. It is characterized by a fully automated workflow with integrated analytical instrumentation. It uses shaken 6-24 well plates as bioreactors which can be run in batch and fed-batch mode with a capacity of up to 768 reactors in parallel [1,2]. A wide ranging analytical portfolio is available to monitor cell culture processes and also a cooperation with internal high throughput (HT) analytic groups to characterize product quality. The current work focuses on expanding the analytical portfolio to develop control mechanisms for automated cell culture processes. Besides setting up a robust method for pH measurement we evaluated different spectroscopic techniques like Raman or infrared as fast and powerful analytical tools.

Development and application of an automated, multiwell plate based screening system for suspension cell culture

The already presented automated, multiwell plate (MWP) based screening system for suspension cell culture is now routinely used in process development. It is characterized by a fully automated workflow with integrated analytical instrumentation and uses shaken 6-24 well plates as bioreactors which can be run in batch and fed-batch mode with a capacity of up to 384 reactors in parallel [1]. A wide ranging analytical portfolio is available to monitor cell culture processes and to characterize product quality. Assays running on the screening system comprise the determination of cell concentration and viability, quantification of nutrients and metabolites as well as detection of apoptosis level and staining of organelles. Additionally a RT-qPCR method has been setup to measure gene expression level in a high throughput manner. Having a large network in-house to high throughput groups of the analytical department a lot of advanced methods can easily be performed like chromatographic and mass spectrometry to characterize product quality. Current work focuses on expanding the analytical portfolio to develop control strategies for automated cell culture processes. Besides setting up a robust method for pH measurement we evaluate different spectroscopic techniques like Raman, infrared or 2D fluorescence as fast and powerful analytical tools.

The Nature of Customer Portfolios: Towards New Understanding of Firms' Exchange Contexts

Purpose: Customer portfolios represent one of the central contexts for business marketing and thereby an important focus for industrial marketing research. In this article, the authors used the concept of customer portfolio to create new insights into the relational context of exchange where companies operate and manage their business. The study examines the general nature of firms' total customer portfolios in contrast to analytical portfolio studies that focus on value-segmentation of the firm's customer base. The authors contribute to the existing, largely conceptual knowledge on the topic by identifying key dimensions that systematically characterize firms' customer portfolios and building an empirical classification of portfolios. Methodology: The study is based on a nationwide cross-industry survey. The authors use cluster analysis to create the classification and discriminant analysis to investigate the importance of the portfolio dimensions for the identified clusters. Findings: The results show that firms' customer portfolios vary markedly, both in terms of structural and relational complexity. The differences denote a clear emphasis on either focused partnership, dynamic-networked, or broad market-like portfolios, posing significantly different drivers and challenges for customer relationship management. The identified dimensions, their measures, and the empirical classification offer important implications for business marketing research and practice.

Neutrophil gelatinase-associated lipocalin (NGAL): the clinician’s perspective

Objectives The field of artificial intelligence (AI) has grown in the past 10 years. Despite the crucial role of laboratory diagnostics in clinical decision-making, we found that the majority of AI studies focus on surgery, radiology, and oncology, and there is little attention given to AI integration into laboratory medicine. Methods We dedicated a session at the 3rd annual European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) strategic conference in 2022 to the topic of AI in the laboratory of the future. The speakers collaborated on generating a concise summary of the content that is presented in this paper. Results The five key messages are (1) Laboratory specialists and technicians will continue to improve the analytical portfolio, diagnostic quality and laboratory turnaround times; (2) The modularized nature of laboratory processes is amenable to AI solutions; (3) Laboratory sub-specialization continues and from test selection to interpretation, tasks increase in complexity; (4) Expertise in AI implementation and partnerships with industry will emerge as a professional competency and require novel educational strategies for broad implementation; and (5) regulatory frameworks and guidances have to be adopted to new computational paradigms. Conclusions In summary, the speakers opine that the ability to convert the value-proposition of AI in the laboratory will rely heavily on hands-on expertise and well designed quality improvement initiative from within laboratory for improved patient care.

Time Cardinality Constrained Mean-Variance Dynamic Portfolio Selection

An investor does not always invest in risky assets in all time periods, often due to the management fee charged for hiring an agent in managing his investment in risky assets. Motivated by this observed common phenomenon, this paper considers the time cardinality constrained mean-variance dynamic portfolio selection problem (TCCMV) in which the number of time periods where investment in risky assets is allowed is limited. Both the analytical optimal portfolio policy and the analytical expression of the efficient mean-variance frontier are derived for TCCMV problem. By implementing such a solution procedure for different cardinalities, the mean-variance dynamic portfolio selection problem with management fees can be efficiently solved.

Optimal portfolio model under compound jump processes

PurposeThe purpose of this paper is to research the optimal portfolio proportion for the optimal investment model and the optimal consumption investment strategies for the optimal consumption investment model under compound‐jump processes.Design/methodology/approachTraditionally, the price of risky security or asset is often modeled as geometric Brownian motion. However, the analysis of stock price evolution reveals sudden and rare breaks logically accounted for by exogenous events on information. It is natural to model such behavior by means of a point process, or, more simply, by a Poisson process, which has jumps of constant size occurring at rare and unpredictable intervals. Assume that the price of risky security stock is modeled by a compound‐jump process, the renew process theory is chosen to solve the optimal investment model, the HJB equation is chosen for the optimal consumption investment model.FindingsDerive the analytical optimal portfolio proportion for the reduction model of optimal investment. The optimal consumption investment strategies are given by some equations for the optimal consumption investment model.Research limitations/implicationsAccessibility and availability of data are the main limitations which model will be applied.Practical implicationsThe results obtained in this paper could be used as a guide to actual portfolio management.Originality/valueThe new approach for the optimal portfolio model under compound‐jump processes. The paper is aimed at actual portfolio managers.

Microeconomic foundations of corporate strategy

AbstractThis paper examines several approaches to corporate strategic planning in the context of their relationship to the microeconomic theory of the firm, and discusses the implications of that theory for corporate strategy in general. Specifically, three types of analytical tools are discussed: (1) analytical portfolio models; (2) business simulation models; and (3) optimization models. Examples of how the three types of tools are used in corporate decision‐making are given, and certain limitations are cited. The limitations include, for example, the inability of some of the models to deal with interdependencies across business units in production resources or output demand. Finally, the paper examines the approach to strategic planning known as the ‘strategy matrix’, which explicitly allows for interdependices across business units. The use of the strategy matrix by a major petroleum company is discussed in some detail.

Strategic planning and forecasting

AbstractThis paper examines the role of forecasting in strategic planning through the use of analytical portfolio models and corporate simulation models. It also outlines a conceptual framework on which it may be possible to develop a theory of strategic planning—namely, microeconomic theory. It, finally, describes a practical application of this theoretical framework, known as the strategy matrix. It concludes by noting the importance of forecasting as an input to the strategy matrix.