Decision-support tool for assessing biomanufacturing strategies under uncertainty: stainless steel versus disposable equipment for clinical trial material preparation

This paper presents the application of a decision-support tool, SIMBIOPHARMA, for assessing different manufacturing strategies under uncertainty for the production of biopharmaceuticals. SIMBIOPHARMA captures both the technical and business aspects of biopharmaceutical manufacture within a single tool that permits manufacturing alternatives to be evaluated in terms of cost, time, yield, project throughput, resource utilization, and risk. Its use for risk analysis is demonstrated through a hypothetical case study that uses the Monte Carlo simulation technique to imitate the randomness inherent in manufacturing subject to technical and market uncertainties. The case study addresses whether start-up companies should invest in a stainless steel pilot plant or use disposable equipment for the production of early phase clinical trial material. The effects of fluctuating product demands and titers on the performance of a biopharmaceutical company manufacturing clinical trial material are analyzed. The analysis highlights the impact of different manufacturing options on the range in possible outcomes for the project throughput and cost of goods and the likelihood that these metrics exceed a critical threshold. The simulation studies highlight the benefits of incorporating uncertainties when evaluating manufacturing strategies. Methods of presenting and analyzing information generated by the simulations are suggested. These are used to help determine the ranking of alternatives under different scenarios. The example illustrates the benefits to companies of using such a tool to improve management of their R&D portfolios so as to control the cost of goods.     

A protocol for horizontal averaging of unit process data—including estimates for uncertainty

Purpose

Quantitative uncertainties are a direct consequence of averaging, a common procedure when building life cycle inventories (LCIs). This averaging can be amongst locations, times, products, scales or production technologies. To date, however, quantified uncertainties at the unit process level have largely been generated using a Numerical Unit Spread Assessment Pedigree (NUSAP) approach and often disregard inherent uncertainties (inaccurate measurements) and spread (variability around means).

Methods

A decision tree for primary and secondary data at the unit process level was initially created. Around this decision tree, a protocol was developed with the recognition that dispersions can be either results of inherent uncertainty, spread amongst data points or products of unrepresentative data. In order to estimate the characteristics of uncertainties for secondary data, a method for weighting means amongst studies is proposed. As for unrepresentativeness, the origin and adaptation of NUSAP to the field of life cycle assessment are discussed, and recommendations are given.

Results and discussion

By using the proposed protocol, cross-referencing of outdated data is avoided, and user influence on results is reduced. In the meantime, more accurate estimates can be made for horizontally averaged data with accompanying spread and inherent uncertainties, as these deviations often contribute substantially towards the overall dispersion.

Conclusions

In this article, we highlight the importance of including inherent uncertainties and spread alongside the NUSAP pedigree. As uncertainty data often are missing in LCI literature, we here describe a method for evaluating these by taking several reported values into account. While this protocol presents a practical way towards estimating overall dispersion, better reporting in literature is promoted in order to determine real uncertainty parameters.     

Sensitivity coefficient-based uncertainty analysis for multi-functionality in LCA

Purpose

In LCA, a multi-functionality problem exists whenever the environmental impacts of a multi-functional process have to be allocated between its multiple functions. Methods for fixing this multi-functionality problem are controversially discussed because the methods include ambiguous choices. To study the influence of these choices, the ISO standard requires a sensitivity analysis. This work presents an analytical method for analyzing sensitivities and uncertainties of LCA results with respect to the choices made when a multi-functionality problem is fixed.

Methods

The existing matrix algebra for LCA is expanded by explicit equations for methods that fix multi-functionality problems: allocation and avoided burden. For allocation, choices exist between alternative allocation factors. The expanded equations allow calculating LCA results as a function of allocation factors. For avoided burden, choices exist in selecting an avoided burden process from multiple candidates. This choice is represented by so-called aggregation factors. For avoided burden, the expanded equations calculate LCA results as a function of aggregation factors. The expanded equations are used to derive sensitivity coefficients for LCA results with respect to allocation factors and aggregation factors. Based on the sensitivity coefficients, uncertainties due to fixing a multi-functionality problem by allocation or avoided burden are analytically propagated. The method is illustrated using a virtual numerical example.

Results and discussion

The presented approach rigorously quantifies sensitivities of LCA results with respect to the choices made when multi-functionality problems are fixed with allocation and avoided burden. The uncertainties due to fixing multi-functionality problems are analytically propagated to uncertainties in LCA results using a first-order approximation. For uncertainties in allocation factors, the first-order approximation is exact if no loops of the allocated functional flows exist. The contribution of uncertainties due to fixing multi-functionality problems can be directly compared to the uncertainty contributions induced by uncertain process data or characterization factors. The presented method allows the computationally efficient study of uncertainties due to fixing multi-functionality problems and could be automated in software tools.

Conclusions

This work provides a systematic method for the sensitivity analysis required by the ISO standard in case choices between alternative allocation procedures exist. The resulting analytical approach includes contributions of uncertainties in process data, characterization factors, and—in extension to existing methods—uncertainties due to fixing multi-functionality problems in a unifying rigorous framework. Based on the uncertainty contributions, LCA practitioners can select fields for data refinement to decrease the overall uncertainty in LCA results.     

Probabilistic Environmental Risk Assessment for Dibutylphthalate (DBP)

In the risk assessment methods for new and existing chemicals in the European Union (EU), environmental “risk” is characterized by the deterministic quotient of exposure and effects (PEC/PNEC). From a scientific viewpoint, the uncertainty in the risk quotient should be accounted for explicitly in the decision making, which can be done in a probabilistic risk framework. To demonstrate the feasibility and benefits of such a framework, a sample risk assessment for an existing chemical (dibutylphthalate, DBP) is presented in this paper. The example shows a probabilistic framework to be feasible with relatively little extra effort; such a framework also provides more relevant information. The deterministic risk quotients turned out to be worst cases at generally higher than the 95th percentile of the probability distributions. Sensitivity analysis proves to be a powerful tool in identifying the main sources of uncertainty and thus will be effective for efficient further testing. The distributions assigned to the assess ment factors (derivation of the PNEC) dominate the total uncertainty in the risk assessment; uncertainties in the release estimates come second. Large uncertainties are an inherent part of risk assessment that we have to deal with quantitatively. However, the most appropriate way to characterise effects and risks requires further attention. Recommendations for further study are identified.     

Uncertainty in Multi-Pathway Risk Assessment for Combustion Facilities

Multi-pathway risk assessments (MPRAs) of contaminant emissions to the atmosphere consider both direct exposures, via ambient air, and indirect exposures, via deposition to land and water. MPRAs embody numerous interconnected models and parameters. Concatenation of many multiplicative and incompletely defined assumptions and inputs can result in risk estimates with considerable uncertainties, which are difficult to quantify and elucidate. Here, three MPRA case-studies approach uncertainties in ways that better inform context-specific judgments of risk. In the first case, default values predicted implausibly large impacts; substitution of site-specific data within conservative methods resulted in reasonable and intuitive worst-case estimates. In the second, a simpler, clearly worst-case water quality model sufficed to demonstrate acceptable risks. In the third case, exposures were intentionally and transparently overestimated. Choices made within particular MPRAs depend on availability of data as suitable replacements for default assumptions, regulatory requirements, and thoughtful consideration of the concerns of interested stakeholders. Explicit consideration of the biases inherent in each risk assessment lends greater credibility to the assessment results, and can form the bases for evidence-based decision-making.     

Environmental restraints and life strategies: a habitat templet matrix

Summary Four basic environmental restraints on life are deduced from the requirements of life's inherent order laws. Possible life strategies to contend with these restraints are listed. The various combinations of the restraints are subsequently investigated, and appropriate combinations of life strategies are fitted. This model is finally tested against insect case histories in various environments, and is demonstrated to explain some combinations of characteristics of insects in ecosystems not covered by the r-K or r-K-A continua. The role of heterochrony in achieving appropriate life strategies is briefly discussed.     

Unraveling the Chimera: A Corporate View of the Precautionary Principle

Current controversy regarding how and when the precautionary principle should be applied to the introduction of new technology has created a false dichotomy, a dichotomy between conventional, risk-based decision making and an alternative paradigm that seemingly denounces risk assessment. As we compare views of the precautionary principle relative to our own operating standards for ensuring human and environmental safety, we perceive no irreconcilable conflict. Due precaution is entirely consistent with sound, cost-effective management of the risks and uncertainties inherent in new technologies. The principle guides prudent risk management actions under a prescribed set of circumstances, i.e., potentially serious or irreversible risks, or incomplete characterization (high uncertainty). In order to enable technological innovation toward a more sustainable future, it is critical that any preventative measures taken under these circumstances be provisional in nature, pending adequate risk characterization. As with all risk management decisions, we contend that the principle requires consideration of a suite of factors beyond risk assessment, including political, social, legal and cultural considerations to tailor the measures proportionately to the risk at hand. Overall, we are encouraged to find relatively broad agreement in this interpretation with a number of key multinational governmental and trade institutions.     

Planning regional ecosystem sustainability under multiple uncertainties—An interval stochastic credibility-constrained programming approach

Inherent uncertainties in regional ecosystems can affect the modeling processes and the relevant results for managing and planning the real-world problems. In this study, an interval stochastic credibility-constrained programming (ISCP) method is developed for tacking multiple uncertainties expressed as intervals, fuzzy sets and probability distributions. The ISCP method is applied to planning regional ecosystem sustainability in the City of Dongying (China), in which ecosystem services valuation approach is directly incorporated within the optimization processes. Results associated with credibility levels of resources availability and the effects of sensitive ecological factors on the ecosystem services and ecological benefit are analyzed. Results show that a tighter limitation of ecological resources availability could cause lower economic development, particularly for oil field and oil refinery industries. Tradeoffs among social, economic and natural subsystems are also examined under different credibility levels, which are provided for generating optimal strategies in supporting of balancing the city’s economic and ecological sustainable development. Results of sensitivity analysis reveal that runoff coefficient is one of the most sensitive ecological factors in the process of calculating the ecological benefit. The findings can provide scientific bases for the integration of economic and ecological activities as well as the development of regional ecosystem-sustainability strategies.     

A Critical Review of the Traditional Methodology of Cost-Benefit Analysis and a Proposed Alternative

“Risk management” is essential to the decision-making process that prescribes regulations for protecting human health. As a comprehensive decision-making approach, risk management encompasses risk assessment, risk perception, economic factors, and their respective uncertainties. Cost-benefit analysis (CBA) has long been the preferred methodology for evaluating the economic factors associated with such regulations. Within this context, CBA confirms whether or not the “benefit” of a given regulatory option is greater than its “cost.” This article proposes an alternative CBA methodology whose guiding concept is the “optimization” of outcomes for the stakeholders in regulations that aim to protect human health. This article further proposes evaluation criteria for CBA and critiques the traditional and alternative variants against this standard, ultimately to demonstrate the superiority of the latter.     

Using Standard Statistics to Consider Uncertainty in Industry-Based Life Cycle Inventory Databases (7 pp)

Goal, Scope and Background Decision-makers demand information about the range of possible outcomes of their actions. Therefore, for developing Life Cycle Assessment (LCA) as a decision-making tool, Life Cycle Inventory (LCI) databases should provide uncertainty information. Approaches for incorporating uncertainty should be selected properly contingent upon the characteristics of the LCI database. For example, in industry-based LCI databases where large amounts of up-to-date process data are collected, statistical methods might be useful for quantifying the uncertainties. However, in practice, there is still a lack of knowledge as to what statistical methods are most effective for obtaining the required parameters. Another concern from the industry's perspective is the confidentiality of the process data. The aim of this paper is to propose a procedure for incorporating uncertainty information with statistical methods in industry-based LCI databases, which at the same time preserves the confidentiality of individual data. Methods The proposed procedure for taking uncertainty in industry-based databases into account has two components: continuous probability distributions fitted to scattering unit process data, and rank order correlation coefficients between inventory flows. The type of probability distribution is selected using statistical methods such as goodness-of-fit statistics or experience based approaches. Parameters of probability distributions are estimated using maximum likelihood estimation. Rank order correlation coefficients are calculated for inventory items in order to preserve data interdependencies. Such probability distributions and rank order correlation coefficients may be used in Monte Carlo simulations in order to quantify uncertainties in LCA results as probability distribution. Results and Discussion A case study is performed on the technology selection of polyethylene terephthalate (PET) chemical recycling systems. Three processes are evaluated based on CO2 reduction compared to the conventional incineration technology. To illustrate the application of the proposed procedure, assumptions were made about the uncertainty of LCI flows. The application of the probability distributions and the rank order correlation coefficient is shown, and a sensitivity analysis is performed. A potential use of the results of the hypothetical case study is discussed. Conclusion and Outlook The case study illustrates how the uncertainty information in LCI databases may be used in LCA. Since the actual scattering unit process data were not available for the case study, the uncertainty distribution of the LCA result is hypothetical. However, the merit of adopting the proposed procedure has been illustrated: more informed decision-making becomes possible, basing the decisions on the significance of the LCA results. With this illustration, the authors hope to encourage both database developers and data suppliers to incorporate uncertainty information in LCI databases.     

Field trials and tribulations--making sense of the regulations for experimental field trials of transgenic crops in Europe

Transgenic plants that are being developed for commercial cultivation must be tested under field conditions to monitor their effects on surrounding wildlife and conventional crops. Developers also use this opportunity to evaluate the performance of transgenic crops in a typical environment, although this is a matter of commercial necessity rather than regulatory compliance. Most countries have adapted existing regulations or developed new ones to deal specifically with transgenic crops and their commodities. The European Union (EU) is renowned, or perhaps notorious, for having the broadest and most stringent regulations governing such field trials in the world. This reflects its nominal adherence to the precautionary approach, which assumes all transgenic crops carry an inherent risk. Therefore, field trials in the EU need to demonstrate that the risk associated with deploying a transgenic crop has been reduced to the level where it is regarded as acceptable within the narrowly defined limits of the regulations developed and enforced (albeit inconsistently) by national and regional governments, that is, that there is no greater risk than growing an equivalent conventional crop. The involvement of national and regional competent authorities in the decision-making process can add multiple layers of bureaucracy to an already-intricate process. In this review, we use country-based case studies to show how the EU, national and regional regulations are implemented, and we propose strategies that could increase the efficiency of regulation without burdening developers with further unnecessary bureaucracy.     

Meeting the challenge of quantitative risk assessment for genetic control techniques: a framework and some methods applied to the common Carp (Cyprinus carpio) in Australia

In Australia the European carp is widespread, environmentally damaging and difficult to control. Genetic control options are being developed for this species but risk-assessment studies to support these options have been limited. The key science challenge in this context is our limited understanding of complex and highly variable ecosystems. Hierarchical models are one way to approach this complexity and heterogeneity. These models treat the factors that determine risk as a joint probability distribution that can be factored into a series of simpler conditional distributions to allow Bayesian inference following observed outcomes. Designing a risk assessment around this approach, however, requires that the assessment endpoints (such as impacts on native species) are measurable, and that monitoring strategies are carefully designed and implemented in order that risk predictions are compared to outcomes. We therefore suggest that an evidence-based framework, supported by careful hazard analysis and quantitative risk assessment, and implemented within a stage-released protocol, is the safest way to move beyond the current emphasis on contained laboratory studies and qualitative risk assessments. We highlight impediments to this approach, and use the non-target impacts of daughterless carp in Australian billabongs as a case study to illustrate three methodological tools that not only provide solutions to some of these impediments but also encourage stakeholder participation in the risk assessment process.     

A Cost-Effectiveness Analysis Evaluating Endoscopic Surveillance for Gastric Cancer for Populations with Low to Intermediate Risk

Background

Gastric cancer (GC) surveillance based on oesophagogastroduodenoscopy (OGD) appears to be a promising strategy for GC prevention. By evaluating the cost-effectiveness of endoscopic surveillance in Singaporean Chinese, this study aimed to inform the implementation of such a program in a population with a low to intermediate GC risk.

Methods

Using a reference strategy of no OGD intervention, we evaluated four strategies: 2-yearly OGD surveillance, annual OGD surveillance, 2-yearly OGD screening and 2-yearly screening plus annual surveillance in Singaporean Chinese aged 50-69 years. From a perspective of the healthcare system, Markov models were built to simulate the life experience of the target population. The models projected discounted lifetime costs ($), quality adjusted life year (QALY), and incremental cost-effectiveness ratio (ICER) indicating the cost-effectiveness of each strategy against a Singapore willingness-to-pay of $46,200/QALY. Deterministic and probabilistic sensitivity analyses were used to identify the influential variables and their associated thresholds, and to quantify the influence of parameter uncertainties respectively.

Results

With an ICER of $44,098/QALY, the annual OGD surveillance was the optimal strategy while the 2-yearly surveillance was the most cost-effective strategy (ICER  =  $25,949/QALY). The screening-based strategies were either extendedly dominated or cost-ineffective. The cost-effectiveness heterogeneity of the four strategies was observed across age-gender subgroups. Eight influential parameters were identified each with their specific thresholds to define the choice of optimal strategy. Accounting for the model uncertainties, the probability that the annual surveillance is the optimal strategy in Singapore was 44.5%.

Conclusion

Endoscopic surveillance is potentially cost-effective in the prevention of GC for populations at low to intermediate risk. Regarding program implementation, a detailed analysis of influential factors and their associated thresholds is necessary. Multiple strategies should be considered in order to recommend the right strategy for the right population.     

Navigating uncertainty in environmental composite indicators

Composite indicators (CIs) are increasingly used to measure and track environmental systems. However, they have faced criticism for not accounting for uncertainties and their often arbitrary nature. This review highlights methodological challenges and uncertainties involved in creating CIs and provides advice on how to improve future CI development in practice. Linguistic and epistemic uncertainties enter CIs at different stages of development and may be amplified or reduced based on subjective decisions during construction. Lack of transparency about why decisions were made can risk impeding proper review and iterative development. Research on uncertainty in CIs currently focuses on how different construction decisions affect the overall results and is explored using sensitivity and uncertainty analysis. Much less attention is given to uncertainties arising from the theoretical framework underpinning the CI, and the sub-indicator selection process. This often lacks systematic rigour, repeatability and clarity. We recommend use of systems modelling as well as systematic elicitation and engagement during CI development in order to address these issues. Composite indicators make trends in complex environmental systems accessible to wider stakeholder groups, including policy makers. Without proper discussion and exposure of uncertainty, however, they risk misleading their users through false certainty or misleading interpretations. This review offers guidance for future environmental CI construction and users of existing CIs, hence supporting their iterative development and effective use in policy-making.     

Robust operation of fed batch fermenters

Optimisation of fed batch fermenters can substantially increase the profitability of these processes. Optimal control of a fed batch fermenter is usually based on a nominal process model. Parameter uncertainties are not taken into account. Simulation studies show that results obtained with fixed nominal model parameters can be quite sensitive to the uncertainty in parameter values. This paper presents a method for obtaining robust optimal control profiles in the presence of uncertainty in the model parameters. The proposed approach is illustrated with a case study. It is also shown that feedback controllers can reduce the effect of the uncertainties.     

城市景观生态风险评估框架与实践——以北京天坛地区为例

当前，城市景观生态风险研究缺少科学合理、方便实用的评估框架。作者基于景观生态风险评估基本范式，明确了城市景观生态服务价值的测算方法，分析了引起生态损害的自然因素和人类活动因素，形成了城市景观生态风险评估的技术框架和参数体系；继而以北京天坛地区为研究区，开展了典型城市景观生态风险的定量评估。结果表明：（1）天坛地区景观生态价值总量约为2.41亿元。区域的历史文化价值最高，教育和美学景观价值紧随其后。（2）城市景观生态受损概率呈现"北高南低"的空间分布格局。生态受损概率的高值区面积占整个区域总面积的22.2%，主要分布在珠市口、磁器口和崇文门附近区域。（3）城市景观生态风险呈现"北低南高"的空间分布格局。高风险区主要分布在天坛公园内的文物建筑周边。本研究提供了一个可参考的城市景观生态风险评估应用框架，对生态风险评估中的不确定性进行了讨论，研究针对天坛案例区的具体结论有助于城市管理者避免潜在的风险。     

Relevance of the 10X Uncertainty Factor to the Risk Assessment of Drugs Used by Children and Geriatrics

Conventional risk assessment practices utilize a tenfold uncertainty factor (UF) to extrapolate from the general human population to sensitive subgroups, such as children and geriatrics. This study evaluated whether the tenfold UF can be reduced when pharmacokinetic and pharmacodynamic data for pharmaceuticals used by children and geriatrics are incorporated into the risk assessment for human sensitivity. Composite factors (kinetics X dynamics) were calculated from data-derived values for bumetanide, furosemide, metoprolol, atenolol, naproxen, and ibuprofen. For the compounds examined, all of the composite factors were lower than 10. Furthermore, 8 of the 12 composite factors were less than 5.5. Incorporation of human kinetic and dynamic data into risk assessment can aid in reducing the uncertainties associated with sensitive subgroups and further study is encouraged.     

Set-base dynamical parameter estimation and model invalidation for biochemical reaction networks

Background  

Mathematical modeling and analysis have become, for the study of biological and cellular processes, an important complement to experimental research. However, the structural and quantitative knowledge available for such processes is frequently limited, and measurements are often subject to inherent and possibly large uncertainties. This results in competing model hypotheses, whose kinetic parameters may not be experimentally determinable. Discriminating among these alternatives and estimating their kinetic parameters is crucial to improve the understanding of the considered process, and to benefit from the analytical tools at hand.     

Uncertainties in the governance of animal disease: an interdisciplinary framework for analysis

Uncertainty is an inherent feature of strategies to contain animal disease. In this paper, an interdisciplinary framework for representing strategies of containment, and analysing how uncertainties are embedded and propagated through them, is developed and illustrated. Analysis centres on persistent, periodic and emerging disease threats, with a particular focus on cryptosporidiosis, foot and mouth disease and avian influenza. Uncertainty is shown to be produced at strategic, tactical and operational levels of containment, and across the different arenas of disease prevention, anticipation and alleviation. The paper argues for more critically reflexive assessments of uncertainty in containment policy and practice. An interdisciplinary approach has an important contribution to make, but is absent from current real-world containment policy.     

Proportional-Integral-Derivative (PID) Control of Secreted Factors for Blood Stem Cell Culture

Clinical use of umbilical cord blood has typically been limited by the need to expand hematopoietic stem and progenitor cells (HSPC) ex vivo. This expansion is challenging due to the accumulation of secreted signaling factors in the culture that have a negative regulatory effect on HSPC output. Strategies for global regulation of these factors through dilution have been developed, but do not accommodate the dynamic nature or inherent variability of hematopoietic cell culture. We have developed a mathematical model to simulate the impact of feedback control on in vitro hematopoiesis, and used it to design a proportional-integral-derivative (PID) control algorithm. This algorithm was implemented with a fed-batch bioreactor to regulate the concentrations of secreted factors. Controlling the concentration of a key target factor, TGF-β1, through dilution limited the negative effect it had on HSPCs, and allowed global control of other similarly-produced inhibitory endogenous factors. The PID control algorithm effectively maintained the target soluble factor at the target concentration. We show that feedback controlled dilution is predicted to be a more cost effective dilution strategy compared to other open-loop strategies, and can enhance HSPC expansion in short term culture. This study demonstrates the utility of secreted factor process control strategies to optimize stem cell culture systems, and motivates the development of multi-analyte protein sensors to automate the manufacturing of cell therapies.