Tag: Garden grow bags

Disposal of TMV-contaminated materials poses low environmental risk

As batches move from the upstream portion of the facility every 3.44 days, the remaining time left over in the downstream is set as slack time in the model that may be dedicated toward repair, maintenance, etc. The assumptions and results developed in SuperPro were used to calculate the economics of the process described. Table 2 shows the total operating costs segregated individually for upstream and downstream components. Figure 5 displays process category cost contributions graphically, including percentages of total costs. In upstream operations, the largest cost components are utilities and labor , representing 61% and 32% of total upstream costs, respectively. In downstream operations, labor-dependent costs are the highest contributors at 30% of total downstream costs, followed by consumables at 27% of total downstream costs. Overall, the upstream component represents nearly 57% of the total Griffithsin production cost, which is calculated as just over $106/g protein. For a micro-bicide dose of 3 mg, the per-dose manufacturing cost is $0.32, excluding any CMO fee. An environmental health and safety assessment was also conducted for this case study following the method of Biwer and Heinzle and the results are found in Supplementary Tables 2–4 in Supplementary Materials. Overall, the process uses chemicals that are not harmful to people or the environment, as can be seen by the low magnitude of input and output Environmental Factor values in Supplementary Table 4. The biggest causes for concern are TMV in the residual biomass,grow bags for gardening and sodium hydroxide and phosphoric acid used in clean-in-place operations, if released to the environment; however we included costs for a thermal or chemical deactivation step for the TMV-contaminated biomass and pH neutralization for the acid and base cleaning agents which would eliminate the environmental impact of these components.

It should also be noted that the upstream nutrient compounds can be more efficiently recycled to increase nutrient utilization by the plants and reduce water/soil impact. Waste compounds in the downstream process are disposed of through wastewater and bio-waste treatment. An aggregate disposal cost of $0.01 per liter of non-TMV-contaminated aqueous streams and $0.1 per kg of bio-waste is assigned in SuperPro for expenses related to wastewater disposal and thermal/chemical deactivation of bio-waste streams. Compounds introduced during or after the post-inoculation step in the upstream facility are considered as bio-waste since they may contain TMV. This includes spent nutrient solution in the post-inoculation step and retentate streams from plate-and frame and dead-end sterilizing filtration skids. There is extensive industrial experience in disposing of TMV contaminated materials, which can be rendered non-infective by treatment with bleach, heat or detergents, diluted and disposed of as municipal waste .The facility modeled can annually produce 20 kg of the potent antiviral Griffithsin for use in microbicide products. The host used in our modeling was Nicotiana benthamiana. This species was selected because of its aforementioned productivity, but also because our previous report on technoeconomic modeling of Nicotiana-produced therapeutic and industrial products prefaces the work reported herein. In addition, the use of Nicotiana for production of clinical trial materials is also familiar to FDA and other regulatory agencies, thus facilitating Nicotiana’s acceptance in regulation-compliant manufacturing . The API is manufactured in the host Nicotiana benthamiana using tobacco mosaic virus as the expression vector. The upstream plant growth and Griffithsin production operations are adapted from the facility layout detailed byHoltz et al. . Over 158,000 plants are housed in vertically stacked hydroponic grow racks, fitted with high-efficiency LED lights. The environment is controlled and monitored for compliance with good agricultural practices . Each batch of 14,450 plants grows over the course of 38 days and yields a total of 578 kilograms of biomass.

Ninety-five batches are seeded and grown annually, with one batch reaching harvest every 3.44 days. The downstream Griffithsin extraction and purification process is scaled up from the pilot industrial scale process presented by Fuqua et al. . An expression rate of 0.52 grams of Griffithsin per kilogram of biomass and a downstream recovery of 70% were used in the base case and give a combined yield of 0.370 grams of Griffithsin per kilogram of harvested biomass. Sterile filtration and CIP/SIP systems facilitate compliance with cGMP guidelines. Downstream processing commences upon the completion of an upstream batch and takes 39.3 h. The stable final formulation is >99% Griffithsin as the API with negligible endotoxin levels. In the model, the upstream costs account for nearly 57% of the total cost of Griffithsin production. Containing both upstream and downstream losses of the protein could significantly reduce COGS. Approximately 12% of the protein API is non-liberated from the homogenized biomass and 18% is lost during downstream polishing steps. Based on the data and assumptions employed in the current analysis, the unit production cost of Griffithsin is estimated to be $0.32 per dose . The model was based on published designs for a commercial scale facility and pilot-scale data on Griffithsin production adapted to the facility described. This type of modeling is useful for determining ranges of API selling price, production capacity and expression level requirements for commercial supply and profitability. In this study we modeled the manufacturing of Griffithsin through a contract manufacturing organization instead of a greenfield build of a new facility because we assumed that that would be the most prudent approach to launching a new product. If the product manufactured using the process modeled is used directly as a vaginal rinse or rectal enema, the additional costs post manufacturing would include transportation, storage, insurance, distribution, marketing, etc., none of which were modeled in this manufacturer-level analysis.

If the Drug Substance produced via the process analyzed is further formulated , or used as a component of another device , those costs and other product-specific costs would be additive and were also excluded from our manufacturer-level analysis. The cost of goods calculated by the current model reflects the manufacturer’s cost of production. We are less certain about the wholesale price of the drug because there is no standard “off- the-shelf ” profit margin that can be added to toll manufacturing cost to arrive at a standardized answer. Often scale up to commercial launch volumes of a product requires additional process development and optimization, validation batches, etc., which lead to negotiated transfer prices depending on volume, duration of engagement, license fees, export duties, and other factors, all of which would impact the cost of bulk Griffithsin. Nevertheless, for this discussion we assumed a manufacturer’s fee of 20% of COGS for a total production cost of bulk Griffithsin Drug Substance of $0.38/dose. Additive formulation, storage, distribution, insurance, marketing, sales margins and other costs could lead to a consumer-level use cost of $1-2/dose . This techno economic analysis emphasized Griffithsin’s use in microbicides because such products arguably represent the most price-constrained applications of this new drug. We cannot define the target retail price of a Griffithsin microbicide; there is no market reference price for micro-bicides since no commercial microbicides yet exist. For perspective,garden grow bags the user cost of a Griffithsin microbicide can be benchmarked against pre-exposure prophylaxis with traditional male condoms and PrEP with micro-bicides containing antiretroviral drugs as a newer alternative. Analyses have been conducted on the cost of prevention modalities and the cost savings to the healthcare system enabled by preventing HIV transmission, with prevention being far more cost effective than treatment in most scenarios . Walensky et al. conducted an analysis of the cost-effectiveness of a Tenofovir based PrEP micro-bicide in South African women. In their cost modeling of a vaginal gel, they multiplied the product cost of $0.32/dose times 2 and by 7.2 to arrive at a product use cost of approximately $5/woman month. However, the price of the microbicide gel used in the study was assumed and region-adjusted and hence pricing in other countries may be different. Terris-Prestholt et al. estimated Tenofovir gel prices of $0.25–0.33 per dose, provided that the gel was used in combination with a condom , from which an additive cost of use of $7–$12/person-month can be derived. Assuming the same average use rate of a Griffithsin containing microbicide applied singly without a condom and priced at $1.00–$2.00 per dose, the cost of use would be $7– <$15/person-month. Whether a higher cost of use discourages adoption of Griffithsin-based microbicides by men and women remains to be shown. A market study by Darroch and Frost of the Alan Guttmacher Institute consisted of detailed interviews of a cross-section of 1,000 sexually active women aged 18–44 in the continental United States. Their statistically rigorous survey identified levels and predictors of women’s concerns about STDs and interest in microbicides, as well as their preferences regarding method characteristics and likelihood of usage versus price of product, with survey sample results extrapolated to the national level.

The results showed that of the estimated 12.6 million women aged 18–44 interested in microbicides and concerned about STDs, including HIV, 11.5 million would still be interested in the method even if it were not 100% effective, and 11.0 million would remain interested even if the microbicide did not protect against STDs other than HIV. The same study found that women’s predicted use of a microbicide was affected by price, but interest was still high at $2 per application, or roughly up to 5-times the average price of a male condom. The survey concluded that more than seven million sexually active women in the USA would be interested in a vaginal microbicide even if the product only protected against HIV, was only 70–80% effective and cost them $2 per application . That conclusion was arrived at in 1999; the $2 per application cost back then would be $3.05 in 2018. One can conclude from these results that there is interest in effective yet inexpensive, self-administered HIV and STD prevention modalities even if such products might cost more than conventional prevention methods. The Darroch and Frost analysis was conducted nearly 20 years ago, and the interviews were limited to women practicing vaginal intercourse. To our knowledge, a more recent study linking likelihood of product use and price sensitivity has not been conducted, or at least not reported, to include other populations of potential microbicide users such as heterosexual couples practicing anal sex or gay men practicing unprotected rectal intercourse. Nevertheless, the 1999 study established an initial price point and price sensitivity for potential users of microbicides in the USA. Griffithsin has a broader spectrum of antiviral activity than HIV-specific PrEP agents, including activity against HSV-2 and HCV, which are co-transmitted with HIV-1 . Hence, Griffithsin might command a higher price due to its broader antiviral activity and its potential to obviate prevention and treatment costs for co-transmitted viruses. In the USA, the cost of the oral PrEP drug Truvada ranges from $1,300 to over $1,700 per month for the uninsured, but treatment is typically covered by insurance with user co-payments of $80–$150 per month. So even if a Griffithsin-containing microbicide sold for $5 per application , a user of 2 packs per month would pay $100 for the microbicide, which is in the range of PrEP, with the potential added benefit of controlling co-transmitted viruses. Consumers in wealthier economies might be receptive to microbicides costing $1–2 or even more per dose; however, consumers in lesser-developed economies might find $1–2/dose to be prohibitive. Hence, absent subsidies, there exists a continuing need to lower COGS for APIs such as Griffithsin. We can conclude that a COGS of <$0.40/dose of Griffithsin DS as determined in this study, and an estimated user cost of $1– 2/dose, might enable at least some simpler formulations of the drug to be economically marketed. For more complex formulations and delivery systems, or for higher doses of the drug, lower COGS for bulk Griffithsin would be desirable. The environmental assessment of the plant-based production of Griffithsin indicates low impact, particularly if the plant nutrient solutions are recycled in a hydroponic system and if waste streams containing TMV are treated in a biowaste heat or chemical treatment process. The assessment method used, although semi-quantitative, utilizes mass input and output stream data generated by SuperPro, along with independent assessment of compound toxicity and/or environmental impact , and allows comparison between alternative production strategies, process configurations or chemical components used in the manufacturing process.

Sample individuals who had left the original study area were tracked throughout Kenya

Note that it is also possible that one might observe positive migration flows into non-agricultural employment even in the case where the true average productivity gap, was negative; in such a case, movers would consist of those with particularly large and positive individual returns to non-agricultural relative to agricultural employment in that time period, or perhaps those who face sufficiently large idiosyncratic preferences for the move, say, those with negative.By this logic, fixed effects estimates will be generally larger than the average population treatment effect. This suggests that estimated gaps based on those who were initially in the agricultural sector are likely to be upper bounds on the magnitude of the true average productivity gap in the population as a whole. Hendricks and Schoellman make a closely related point, arguing that their estimates of the returns to international migration are likely to be upper bounds. In this study, this will likely be the case with the Kenya data where the entire sample lived in rural areas at baseline. In the Indonesia data , which features sorting in both directions , it is in theory possible to observe a non-agricultural premium every time an individual selects into non-agriculture and an agricultural premium every time an individual selects into agriculture. By a parallel logic to above, the selection equation in equation 6 suggests that among those initially working in the non-agricultural sector,garden grow bags we would only observe moves among those that benefit from working in agriculture, i.e. The resulting estimates would then serve as lower bounds on the magnitude of the true average productivity gain to non-agricultural employment. The IFLS provides an ideal test bed to understand the role of these biases in estimating the related urban-rural gap.

In the spirit of Young’s observation that migration flows in both directions, the data allow us to condition on individual birth location and measure the dynamic impacts on wages after migration. The bounding argument above predicts that the estimated urban-rural productivity gap would be larger when estimated for movers from rural to urban areas than it is when estimated for movers from urban to rural areas. We take this prediction to the data and find suggestive evidence for it. This model of selection implies that the true sectoral productivity gap in Indonesia is bounded by these two estimates, generated by movers in each direction. This paper uses detailed panel data from Indonesia and Kenya to estimate worker productivity gaps between the non-agricultural and agricultural sectors, as well as the closely related question of gaps between workers in urban and rural areas. The data we use from both countries is unusually rich, and the long-term panel data structure features high rates of respondent tracking over time. At 250 million, the Southeast Asian country of Indonesia is the fourth most populous in the world, and Kenya is among the most populous Sub-Saharan African countries with approximately 45 million inhabitants. These countries are fairly typical of other low income countries with respect to their labor shares in agriculture, estimated agricultural productivity gaps using national accounts data, and the relationships between these variables and national income levels.The high tracking rates of the datasets we employ allow us to construct multiyear panels of individuals’ location decisions. Moreover, both datasets include employment information on both formal and informal sector employment. The latter is difficult to capture in standard administrative data sources yet often employs a large share of the labor force in low-income countries. If informal employment is more common in rural areas and in agriculture, and is partially missed in national accounts data, this may generate an upward bias in measured sectoral productivity gaps.

Detailed employment data were collected during each survey round. In addition to current employment, the survey included questions on previous employment, allowing us to create up to a 21- year annual employment panel at the individual level from 1988 to 2008. Employment status and sector of employment are available for each year, but in the fourth IFLS round, earnings were collected only for the current job. Therefore, the panel has annual data on employment status and sector of employment from 1988 to 2008, and earnings data annually from 1988 to 2000 and in 2007-08. The IFLS includes information on the respondent’s principal as well as secondary employment. Respondents are asked to include any type of employment, including wage employment, self employment, temporary work, and unpaid family work.In addition to wages and profits, individuals are asked to estimate the value of their compensation in terms of share of harvest, meals provided, transportation allowance, housing and medical benefits, and credit; the main earnings measure we use is thus the comprehensive sum of all wages, profits, and benefits. Individuals are asked to describe the sector of employment for each job. The single largest sector is “agriculture, forestry, fishing, and hunting”: 34% of individuals report it as their primary employment sector, and 47% have secondary jobs in this sector. Agricultural employment is primarily rural: 42% versus 3% of rural and urban individuals, respectively, report working primarily in agriculture . Other common sectors are wholesale, retail, restaurants, and hotels ; social services ; manufacturing ; and construction . These non-agricultural sectors are all more common in urban than rural areas. Men are more likely than women to work in agriculture and less likely to work in wholesale, retail, restaurants, and hotels; and social services. Smaller male-dominated sectors include construction and transportation, storage, and communications . In the analysis that follows, we employ an indicator variable for non-agricultural employment, which equals 1 if a respondent’s main employment is not in agriculture and 0 if main employment is in agriculture. The main analysis sample includes all individuals who are employed and have positive earnings and positive hours worked to ensure that the main variable of interest, the log wage, is defined.

The sample includes 18,211 individuals and 115,897 individual-year observations.In addition to studying wage gaps, we explore consumption gaps to get a broader sense of welfare differences. IFLS consumption data were collected by directly asking households the value in Indonesian Rupiah of all food and non-food purchases and consumption in the last month, similar to consumption data collection in the World Bank’s Living Standards Measurement Surveys.In contrast to the retrospective earnings data in the IFLS, the consumption data are all contemporaneous to the survey. Consumption data were collected at the household level,tomato grow bags which we divide by the number of household members to obtain a per capita measure. The consumption sample includes 38,280 individual-year observations from 19,695 individuals in IFLS rounds 1–4. In the consumption analysis, we expand the sample to also include individuals without current earnings data; we also perform a robustness check on the consumption analysis using the main productivity sample. Data were collected on the respondent’s location at the time of the survey, and all rounds of the IFLS also collected a full history of migration within Indonesia. All residential moves across sub-districts that lasted at least six months are included. Figure 2, Panel A presents a map of Indonesia with each dot representing an IFLS respondent’s residential location. While many respondents live on Java, we observe considerable geographic coverage throughout the country. The IFLS also asked respondents for the main motivation of each move. Family-related reasons are most common at 46%, especially for women , who are more likely than men to state they migrated for marriage. The second most common reason to migrate is for work , with little difference by gender, while migrating for education is less common. We combine data across IFLS rounds to construct a 21-year panel, from 1988 to 2008 with annual information on the person’s location, in line with the employment panel; refer to Kleemans and Kleemans and Magruder for more information on the construction of the IFLS employment and migration panel. We utilize a survey-based measure of urban residence: if the respondent reports living in a “village”, we define the area to be rural, while they are considered urban if they answer “town” or “city.” We present the correspondence between urban residence and employment in the non-agricultural sector in Table 1, Panel A.

In 66 percent of individual-year observations, people are employed in the non-agricultural sector, and in 21 percent of the observations, they live in urban areas. One can see that a substantial portion of rural employment is in both agriculture and non-agricultural work, while urban employment is almost exclusively non-agricultural, as expected. Given the migration focus of the analysis, it is useful to report descriptive statistics both for the main analysis sample, as well as separately for individuals in four mutually exclusive categories : those who always reside in rural areas throughout the IFLS sample period , those who were born in a rural area but move to an urban area at some point , those who are “Always Urban,” and finally, the “Urban-to-Rural Migrants” . As discussed above, the fixed effects analysis is driven by individuals who move between sectors during the sample period. In the main IFLS analysis sample, 80 percent of adults had completed at least primary education, and a quarter had completed secondary education, while tertiary education remain quite limited, at less than 10 percent. Among those who are born in rural areas in columns 2 and 3 , we see that migrants to urban areas are highly positively selected in terms of both educational attainment, and in terms of cognitive ability, with Raven’s Progressive Matrices exam scores roughly 0.2 standard deviation units higher among those who migrate to urban areas, a substantial effect.Migration rates do not differ substantially by gender. These relationships are presented in a regression framework in Table 3, Panel A , and the analogous relationships for moves into non-agricultural employment are also evident . Importantly, the relationship between higher cognitive ability and likelihood of migrating to urban areas holds even conditional on schooling attainment and demographic characteristics , at 99% confidence. This indicates that sorting on difficult to-observe characteristics is relevant in understanding sectoral productivity differences in this context. It is worth noting that if we ignore migrants, individuals who are born and remain in urban areas are far more skilled than those who stay in rural areas. “Always Urban” individuals score over 0.4 standard deviation units higher on Raven’s matrices and have triple the rate of secondary schooling and six times the rate of tertiary education relative to “Always Rural” individuals. The urban-to-rural migrants in Indonesia are also negatively selected relative to those who remain urban residents, which corroborates Young’s claim. These patterns emerge in Table 2, Panel A, where the urban-to rural migrants score lower on all skill dimensions relative to those who remain urban; appendix Tables A1 and A2 report results analogous to Tables 3 and 4, among those individuals born in urban areas. The Kenya Life Panel Survey includes information on 8,999 individuals who attended primary school in western Kenya in the late 1990s and early 2000s, following them through adolescence and into adulthood. These individuals are a representative subset of participants in two primary school based randomized interventions: a scholarship program for upper primary school girls that took place in 2001 and 2002 and a deworming treatment program for primary school students during 1998–2002 . In particular, the KLPS sample contains information on individuals enrolled in over 200 rural primary schools in Busia district at the time of these programs’ launch. According to the 1998 Kenya Demographic and Health Survey, 85% of children in Western Province aged 6–15 were enrolled in school at that time, and Lee et al. show that this area is quite representative of rural Kenya as a whole in terms of socioeconomic characteristics. To date, three rounds of the KLPS have been collected . KLPS data collection was designed with attention to minimizing bias related to survey attrition. Respondents were sought in two separate “phases” of data collection: the “regular tracking phase” proceeded until over 60 percent of respondents had been surveyed, at which point a representative subset of approximately 25 percent of the remaining sample was chosen for the “intensive tracking phase” . These “intensive” individuals receive roughly four times as much weight in the analysis, to maintain representativeness with the original sample.