Acquired von Willebrand syndrome in patients with monoclonal gammopathy of undetermined significance investigated using a mechanistic approach.

BACKGROUND: Acquired von Willebrand syndrome (AVWS) has been reported to occur in association with monoclonal gammopathy, usually of undetermined significance (MGUS). It may present as a type 1 or type 2 von Willebrand factor (VWF) defect depending on the patient's representation of large VWF multimers. MATERIALS AND METHODS: The mathematical model by Galvanin et al., already employed for studying inherited von Willebrand disease (VWD), was used to explore the pathogenic mechanisms behind MGUS-associated AVWS. RESULTS: The patients studied showed significantly reduced VWF levels and function; an increased VWF propeptide to VWF antigen ratio; and all VWF multimers present but in reduced quantities, with the low-molecular-weight VWF forms being significantly more represented than those of higher molecular weight. Our mathematical model revealed a significantly increased VWF elimination rate constant, with values similar to those of type Vicenza VWD. An even more increased VWF proteolysis rate constant was observed, with values one order of magnitude higher than in type 2A VWD but, in contrast, no loss of large multimers. The model predicted the same elimination rate for high- and low-molecular-weight VWF multimers, but proteolysis of the high-molecular-weight forms also contributes to the pool of low-molecular-weight oligomers, which explains why they were relatively over-represented. DISCUSSION: In MGUS-associated AVWS the increase of both clearance and proteolysis contributes to the circulating levels and multimer pattern of VWF, with a phenotype that appears to be a combination of type Vicenza and type 2A VWD. Hence, the mechanisms behind the onset of AVWS seem to differ from those of inherited VWD.

Von Willebrand disease type Vicenza: In search of a classification for the archetype of reduced von Willebrand factor survival.

Type Vicenza von Willebrand disease (VWD) features a von Willebrand factor (VWF) with a very short half-life, and is classified as a form of type 1 VWD. To test the appropriateness of type Vicenza VWD classification, the main features of 17 patients from eight unrelated families were analysed. They had low VWF antigen levels and function (always below 20 U/dl); ristocetin-induced platelet aggregation sometimes normal, sometimes reduced/absent (even in the same patient); normal platelet VWF levels; an increased VWF propeptide to VWF antigen ratio (8.74 ± 1.65 vs. normal 1.04 ± 0.28) and a reduced VWF half-life. Plasma VWF multimer levels were homogeneously reduced, and unusually large VWF multimers were sometimes present. Recombinant p.R1205H VWF showed a normal synthesis, release, function, and multimer pattern, with no ultra-large VWF multimers. The mathematical model by Galvanin et al. was used to explore the kinetic changes in VWF after DDAVP. It showed that the release, but especially the proteolysis (k proteol 1.0-3 ± 2.5-3 vs. normal 4.5-4 ± 6.4-4) and elimination (k el 1.0-2 ± 5.2-3 vs. normal 1.1-3 ± 6.8-4) of type Vicenza VWF were significantly higher than normal. The increased elimination is consistent with the short half-life, while the increased proteolysis was unexpected. As a shorter survival of VWF is wholly responsible for the type Vicenza VWD phenotype (VWF synthesis, structure and function are normal), it might be better to classify it as a type 2 VWD (rather than type 1) to emphasise the greater interaction with clearance receptors as a new VWF functional defect.

A Mechanistic Model to Quantify von Willebrand Factor Release, Survival and Proteolysis in Patients with von Willebrand Disease.

A reduced von Willebrand factor (VWF) synthesis or survival, or its increased proteolysis, alone or in combination, contributes to the development of von Willebrand disease (VWD).We describe a new, simple mechanistic model for exploring how VWF behaves in well-defined forms of VWD after its 1-desamino-8-D-arginine vasopressin (DDAVP)-induced release from endothelial cells. We aimed to ascertain whether the model can consistently predict VWF kinetic changes. The study involved 9 patients with VWD types Vicenza (a paradigmatic form with a reduced VWF survival), 8 type 2B, 2 type 2A-I, 1 type 2A-II (associated with an increased VWF proteolysis), and 42 normal controls, whose VWF levels were measured after a 24-hour-long DDAVP test. The rate constants considered were: k0, associated with the VWF release phase; k1, illustrating the phase of conversion from high- to low-molecular-weight VWF multimers; and ke, associated with the VWF elimination phase. The amount of VWF released (D) was also measured. ke and D were significantly higher in O than in non-O blood group controls; k1 was also higher, but less markedly so. All the parameters were accelerated in type Vicenza, especially ke (p < 0.0001), which explains the significant reduction in VWF half-life. In types 2B and 2A-II, k1 was one order of magnitude higher than in controls, which explains their loss of large VWF multimers. All parameters except ke were lower in type 2A-I.The proposed mechanistic model clearly describes the altered biochemical pathways in well-characterized VWD, prompting us to suggest that it might help clarify elusive forms of VWD too.

Formulation Screening and Freeze-Drying Process Optimization of Ginkgolide B Lyophilized Powder for Injection.

The purpose of this study was to prepare ginkgolide B (GB) lyophilized powder for injection with excellent appearance and stable quality through a formulation screening and by optimizing the freeze-drying process. Cremophor EL as a solubilizer, PEG 400 as a latent solvent, and mannitol as an excipient were mixed to increase the solubility of GB in water to more than 18 times (about from 2.5 × 10-4 mol/L (0.106 mg/mL) to 1.914 mg/mL). Formulation screening was conducted by orthogonal design where the content of GB in the solution before lyophilization (using external standard method of HPLC) and reconstitution time after lyophilization were the two evaluation indexes. The optimized formulations were GB in an amount of 2 mg/mL, Cremophor EL in an amount of 16% (v/v), PEG 400 in an amount of 9% (v/v), mannitol in an amount of 8% (w/v), and the solution pH of 6.5. Through four single-factor experiments (GB adding order, preparation temperature of GB solution, adding amount, and adsorption time of activated carbon), the preparation process of GB solution was confirmed. The glass transition temperature of maximally GB freeze-concentrated solution was - 17.6°C through the electric resistance method. GB lyophilized powder began to collapse at - 14.0°C, and the fully collapsed temperature was - 13.0°C, which were determined by freeze-drying microscope. When the collapse temperature was determined, the primary drying temperature was obtained. Thereby, the freeze-drying curve of GB lyophilized powder was initially identified. The freeze-drying process was optimized by orthogonal design, the qualified product appearance and residual moisture content were the two evaluation indexes. The optimized process parameters and process were (1) shelf temperature, decreased from room temperature to - 45.0°C, at 0.5°C/min in 2 h; (2) shelf temperature increased from - 45.0 to - 25.0°C, at 0.1°C/min, maintained for 3 h, and the chamber pressure was held at 10 Pa; (3) shelf temperature was increased from - 25.0 to - 15.0°C at 0.1 °C/min, maintained for 4 h, and the chamber pressure was held at 10 Pa; and (4) shelf temperature was increased from - 15.0 to 20.0°C at 1.0 °C/min, maintained for 4 h, and the chamber pressure was raised up to 80 Pa. In these lyophilization process conditions, the products complied with relevant provisions of the lyophilized powders for injection. Meanwhile, the reproducibility was satisfactory. Post-freezing annealing had no significantly beneficial effects on shortening the freeze-drying cycle and improving the quality of GB lyophilized powder.

A general model-based design of experiments approach to achieve practical identifiability of pharmacokinetic and pharmacodynamic models.

The use of pharmacokinetic (PK) and pharmacodynamic (PD) models is a common and widespread practice in the preliminary stages of drug development. However, PK-PD models may be affected by structural identifiability issues intrinsically related to their mathematical formulation. A preliminary structural identifiability analysis is usually carried out to check if the set of model parameters can be uniquely determined from experimental observations under the ideal assumptions of noise-free data and no model uncertainty. However, even for structurally identifiable models, real-life experimental conditions and model uncertainty may strongly affect the practical possibility to estimate the model parameters in a statistically sound way. A systematic procedure coupling the numerical assessment of structural identifiability with advanced model-based design of experiments formulations is presented in this paper. The objective is to propose a general approach to design experiments in an optimal way, detecting a proper set of experimental settings that ensure the practical identifiability of PK-PD models. Two simulated case studies based on in vitro bacterial growth and killing models are presented to demonstrate the applicability and generality of the methodology to tackle model identifiability issues effectively, through the design of feasible and highly informative experiments.

On the use of continuous glucose monitoring systems to design optimal clinical tests for the identification of type 1 diabetes models.

The identification of individual parameters of detailed physiological models of type 1 diabetes can be carried out by clinical tests designed optimally through model-based design of experiments (MBDoE) techniques. So far, MBDoE for diabetes models has been considered for discrete glucose measurement systems only. However, recent advances on sensor technology allowed for the development of continuous glucose monitoring systems (CGMSs), where glucose measurements can be collected with a frequency that is practically equivalent to continuous sampling. To specifically address the features of CGMSs, in this paper the optimal clinical test design problem is formulated and solved through a continuous, rather than discrete, approach. A simulated case study is used to assess the impact of CGMSs both in the optimal clinical test design problem and in the subsequent parameter estimation for the identification of a complex physiological model of glucose homeostasis. The results suggest that, although the optimal design of a clinical test is simpler if continuous glucose measurements are made available through a CGMS, the noise level and formulation may make continuous measurements less suitable for model identification than their discrete counterparts.

Optimal design of clinical tests for the identification of physiological models of type 1 diabetes in the presence of model mismatch.

How to design a clinical test aimed at identifying in the safest, most precise and quickest way the subject-specific parameters of a detailed model of glucose homeostasis in type 1 diabetes is the topic of this article. Recently, standard techniques of model-based design of experiments (MBDoE) for parameter identification have been proposed to design clinical tests for the identification of the model parameters for a single type 1 diabetic individual. However, standard MBDoE is affected by some limitations. In particular, the existence of a structural mismatch between the responses of the subject and that of the model to be identified, together with initial uncertainty in the model parameters may lead to design clinical tests that are sub-optimal (scarcely informative) or even unsafe (the actual response of the subject might be hypoglycaemic or strongly hyperglycaemic). The integrated use of two advanced MBDoE techniques (online model-based redesign of experiments and backoff-based MBDoE) is proposed in this article as a way to effectively tackle the above issue. Online model-based experiment redesign is utilised to exploit the information embedded in the experimental data as soon as the data become available, and to adjust the clinical test accordingly whilst the test is running. Backoff-based MBDoE explicitly accounts for model parameter uncertainty, and allows one to plan a test that is both optimally informative and safe by design. The effectiveness and features of the proposed approach are assessed and critically discussed via a simulated case study based on state-of-the-art detailed models of glucose homeostasis. It is shown that the proposed approach based on advanced MBDoE techniques allows defining safe, informative and subject-tailored clinical tests for model identification, with limited experimental effort.